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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.
94 lines
3.2 KiB
Markdown
94 lines
3.2 KiB
Markdown
# Module 18: Compression - Model Size Optimization
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## Overview
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Reduce model size by 90% while maintaining accuracy through pruning and distillation. Learn how production systems deploy efficient models at scale.
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## What You'll Build
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- **Magnitude Pruner**: Remove unimportant weights
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- **Structured Pruning**: Remove entire channels/layers
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- **Knowledge Distillation**: Transfer knowledge to smaller models
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- **Sparse Inference**: Efficient computation with pruned models
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## Learning Objectives
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1. **Sparsity Patterns**: Structured vs unstructured pruning
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2. **Pruning Strategies**: Magnitude, gradient, lottery ticket
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3. **Distillation**: Teacher-student knowledge transfer
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4. **Deployment**: Optimize sparse models for production
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## Prerequisites
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- Module 10: Training (models to compress)
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- Module 17: Precision (understanding of optimization tradeoffs)
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## Key Concepts
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### Magnitude-Based Pruning
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```python
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# Remove 90% of smallest weights
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def prune_magnitude(model, sparsity=0.9):
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for layer in model.layers:
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threshold = torch.quantile(abs(layer.weight), sparsity)
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mask = abs(layer.weight) > threshold
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layer.weight *= mask # Zero out small weights
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```
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### Structured Pruning
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```python
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# Remove entire filters/channels
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def prune_structured(conv_layer, num_filters_to_remove):
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# Compute filter importance (L2 norm)
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importance = conv_layer.weight.norm(dim=(1,2,3))
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# Keep only important filters
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keep_indices = importance.topk(n_keep).indices
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conv_layer.weight = conv_layer.weight[keep_indices]
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```
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### Knowledge Distillation
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```python
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# Small student learns from large teacher
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teacher = LargeModel() # 100M parameters
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student = SmallModel() # 10M parameters
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# Student learns both from labels and teacher
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loss = alpha * cross_entropy(student(x), y) + \
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beta * kl_divergence(student(x), teacher(x))
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```
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## Performance Impact
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- **Model Size**: 10x reduction with pruning
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- **Inference Speed**: 3-5x faster with structured pruning
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- **Accuracy**: Maintain 95%+ of original performance
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- **Memory**: Deploy large models on edge devices
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## Real-World Applications
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- **MobileNet**: Designed for mobile deployment
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- **DistilBERT**: 60% faster, 97% performance
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- **Lottery Ticket Hypothesis**: Finding efficient subnetworks
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- **Neural Architecture Search**: Automated compression
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## Module Structure
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1. **Sparsity Theory**: Why neural networks are compressible
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2. **Magnitude Pruning**: Simple but effective compression
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3. **Structured Pruning**: Hardware-friendly sparsity
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4. **Knowledge Distillation**: Learning from larger models
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5. **Deployment**: Optimizing sparse models
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## Hands-On Projects
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```python
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# Project 1: Prune your CNN
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cnn = load_model("cifar10_cnn.pt")
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pruned = progressive_prune(cnn, target_sparsity=0.9)
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print(f"Parameters: {count_params(cnn)} → {count_params(pruned)}")
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print(f"Accuracy: {evaluate(cnn)}% → {evaluate(pruned)}%")
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# Project 2: Distill transformer to CNN
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teacher = TinyTransformer()
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student = SimpleCNN()
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distilled = distill(teacher, student, data_loader)
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```
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## Success Criteria
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- ✅ Achieve 90% sparsity with <5% accuracy loss
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- ✅ 3x inference speedup with structured pruning
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- ✅ Successfully distill large models to small ones
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- ✅ Deploy compressed models efficiently |