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This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
38 lines
1.5 KiB
YAML
38 lines
1.5 KiB
YAML
name: "acceleration"
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title: "Hardware Acceleration and Kernel Optimization"
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description: "Learn hardware acceleration principles through cache-friendly algorithms, vectorization, and backend systems"
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learning_objectives:
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- "Understand CPU cache hierarchy and memory access performance bottlenecks"
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- "Implement cache-friendly blocked matrix multiplication algorithms"
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- "Build vectorized operations with optimized memory access patterns"
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- "Design transparent backend systems for automatic optimization selection"
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- "Measure and quantify real performance improvements scientifically"
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- "Apply systems thinking to optimization decisions in ML workflows"
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prerequisites:
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- "Module 2: Tensor operations and NumPy fundamentals"
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- "Module 4: Linear layers and matrix multiplication"
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- "Understanding of basic algorithmic complexity (O notation)"
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estimated_time: "3-4 hours"
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difficulty: "Advanced"
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tags:
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- "performance"
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- "optimization"
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- "systems"
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- "hardware"
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- "acceleration"
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- "cache"
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- "vectorization"
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- "backends"
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exports:
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- "blocked_matmul"
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- "vectorized_add"
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- "optimized_relu"
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- "ComputeBackend"
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- "OptimizedBackend"
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- "AccelerationCompetition"
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assessment:
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- "Implement blocked matrix multiplication with measurable speedups"
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- "Build vectorized operations avoiding Python loops"
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- "Create backend system for transparent optimization"
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- "Design competition framework for kernel comparisons"
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- "Analyze optimization principles and real-world applications" |