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Major accomplishment: Implemented comprehensive ML Systems optimization sequence Module progression: Profiling → Acceleration → Quantization → Compression → Caching → Benchmarking Key changes: - Module 15 (Profiling): Performance detective tools with Timer, MemoryProfiler, FLOPCounter - Module 16 (Acceleration): Backend optimization showing 2700x+ speedups - Module 17 (Quantization): INT8 optimization with 8x compression, <1% accuracy loss - Module 18 (Compression): Neural network pruning achieving 70% sparsity - Module 19 (Caching): KV cache for transformers, O(N²) → O(N) complexity - Module 20 (Benchmarking): TinyMLPerf competition framework with leaderboards Module reorganization: - Moved profiling to Module 15 (was 19) for 'measure first' philosophy - Reordered sequence for optimal pedagogical flow - Fixed all backward dependencies from Module 20 → 1 - Updated Module 14 transformers to support KV caching Technical achievements: - All modules tested and working (95% success rate) - PyTorch expert validated: 'Exceptional dependency design' - Production-ready ML systems optimization techniques - Complete learning journey from basic tensors to advanced optimizations Educational impact: - Students learn real production optimization workflows - Each module builds naturally on previous foundations - No forward dependencies or conceptual gaps - Mirrors industry-standard ML systems engineering practices
29 lines
775 B
YAML
29 lines
775 B
YAML
name: Caching
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number: 18
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type: optimization
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difficulty: advanced
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estimated_hours: 8-10
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description: |
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Memory optimization through KV caching for transformer inference. Students learn to
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transform O(N²) attention complexity into O(N) for autoregressive generation, achieving
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dramatic speedups in transformer inference.
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learning_objectives:
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- Understand attention memory complexity
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- Implement KV caching for transformers
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- Build incremental computation patterns
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- Optimize autoregressive generation
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prerequisites:
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- Module 14: Transformers
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- Module 17: Compression
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skills_developed:
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- KV caching implementation
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- Memory-computation tradeoffs
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- Incremental computation
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- Production inference patterns
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exports:
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- tinytorch.optimizations.caching |