Following module developer guidelines, added comprehensive visual diagrams:
1. Text-to-Numbers Pipeline (Introduction):
- Added full boxed diagram showing 4-step tokenization process
- Clear visual flow from human text to numerical IDs
- Each step explained inline with the diagram
2. Character Tokenization Process:
- Step-by-step vocabulary building visualization
- Shows corpus → unique chars → vocab with IDs
- Encoding process with ID lookup visualization
- Decoding process with reverse lookup
- All in clear nested boxes
3. BPE Training Algorithm:
- Comprehensive 4-step process with nested boxes
- Pair frequency analysis with bar charts (████)
- Before/After merge visualizations
- Iteration examples showing vocabulary growth
- Final results with key insights
4. Memory Layout for Embedding Tables:
- Visual bars showing relative memory sizes
- Character (204KB) vs BPE-50K (102MB) vs Word-100K (204MB)
- Shows fp32/fp16/int8 precision trade-offs
- Real production model examples (GPT-2/3, BERT, T5, LLaMA)
- Clear table format for comparison
Educational improvements:
- More visual, less text-heavy
- Clearer step-by-step flows
- Better intuition building
- Production context throughout
- Following module developer ASCII diagram patterns
Students now see:
- HOW tokenization works (not just WHAT)
- WHY different strategies exist
- WHAT the memory implications are
- HOW production models make these choices
PROBLEM:
- nbdev requires #| export directive on EACH cell to export when using # %% markers
- Cell markers inside class definitions split classes across multiple cells
- Only partial classes were being exported to tinytorch package
- Missing matmul, arithmetic operations, and activation classes in exports
SOLUTION:
1. Removed # %% cell markers INSIDE class definitions (kept classes as single units)
2. Added #| export to imports cell at top of each module
3. Added #| export before each exportable class definition in all 20 modules
4. Added __call__ method to Sigmoid for functional usage
5. Fixed numpy import (moved to module level from __init__)
MODULES FIXED:
- 01_tensor: Tensor class with all operations (matmul, arithmetic, shape ops)
- 02_activations: Sigmoid, ReLU, Tanh, GELU, Softmax classes
- 03_layers: Linear, Dropout classes
- 04_losses: MSELoss, CrossEntropyLoss, BinaryCrossEntropyLoss classes
- 05_autograd: Function, AddBackward, MulBackward, MatmulBackward, SumBackward
- 06_optimizers: Optimizer, SGD, Adam, AdamW classes
- 07_training: CosineSchedule, Trainer classes
- 08_dataloader: Dataset, TensorDataset, DataLoader classes
- 09_spatial: Conv2d, MaxPool2d, AvgPool2d, SimpleCNN classes
- 10-20: All exportable classes in remaining modules
TESTING:
- Test functions use 'if __name__ == "__main__"' guards
- Tests run in notebooks but NOT on import
- Rosenblatt Perceptron milestone working perfectly
RESULT:
✅ All 20 modules export correctly
✅ Perceptron (1957) milestone functional
✅ Clean separation: development (modules/source) vs package (tinytorch)
- 09_spatial: Export Conv2d, MaxPool2d, AvgPool2d only
- 10_tokenization: Export Tokenizer, CharTokenizer, BPETokenizer only
- 11_embeddings: Export Embedding, PositionalEncoding only
Continues professional selective export pattern. Clean public APIs,
development utilities remain in development environment.
- Remove circular imports where modules imported from themselves
- Convert tinytorch.core imports to sys.path relative imports
- Only import dependencies that are actually used in each module
- Preserve documentation imports in markdown cells
- Use consistent relative path pattern across all modules
- Remove hardcoded absolute paths in favor of relative imports
Affected modules: 02_activations, 03_layers, 04_losses, 06_optimizers,
07_training, 09_spatial, 12_attention, 17_quantization
