Package exports:
- Fix tinytorch/__init__.py to export all required components for milestones
- Add Dense as alias for Linear for compatibility
- Add loss functions (MSELoss, CrossEntropyLoss, BinaryCrossEntropyLoss)
- Export spatial operations, data loaders, and transformer components
Test infrastructure:
- Create tests/conftest.py to handle path setup
- Create tests/test_utils.py with shared test utilities
- Rename test_progressive_integration.py files to include module number
- Fix syntax errors in test files (spaces in class names)
- Remove stale test file referencing non-existent modules
Documentation:
- Update README.md with correct milestone file names
- Fix milestone requirements to match actual module dependencies
Export system:
- Run tito export --all to regenerate package from source modules
- Ensure all 20 modules are properly exported
Major directory restructure to support both developer and learner workflows:
Structure Changes:
- NEW: src/ directory for Python source files (version controlled)
- Files renamed: tensor.py → 01_tensor.py (matches directory naming)
- All 20 modules moved from modules/ to src/
- CHANGED: modules/ now holds generated notebooks (gitignored)
- Generated from src/*.py using jupytext
- Learners work in notebooks, developers work in Python source
- UNCHANGED: tinytorch/ package (still auto-generated from notebooks)
Workflow: src/*.py → modules/*.ipynb → tinytorch/*.py
Command Updates:
- Updated export command to read from src/ and generate to modules/
- Export flow: discovers modules in src/, converts to notebooks in modules/, exports to tinytorch/
- All 20 modules tested and working
Configuration:
- Updated .gitignore to ignore modules/ directory
- Updated README.md with new three-layer architecture explanation
- Updated export.py source mappings and paths
Benefits:
- Clean separation: developers edit Python, learners use notebooks
- Better version control: only Python source committed, notebooks generated
- Flexible learning: can work in notebooks OR Python source
- Maintains backward compatibility: tinytorch package unchanged
Tested:
- Single module export: tito export 01_tensor ✅
- All modules export: tito export --all ✅
- Package imports: from tinytorch.core.tensor import Tensor ✅
- 20/20 modules successfully converted and exported
Re-exported all modules after restructuring:
- Updated _modidx.py with new module locations
- Removed outdated autogeneration headers
- Updated all core modules (tensor, autograd, layers, etc.)
- Updated optimization modules (quantization, compression, etc.)
- Updated TITO commands for new structure
Changes include:
- 24 tinytorch/ module files
- 24 tito/ command and core files
- Updated references from modules/source/ to modules/
All modules re-exported via nbdev from their new locations.
- Created perceptron_trained.py milestone with full training loop
- Restored tinytorch/core/optimizers.py with Optimizer, SGD, Adam, AdamW classes
- Fixed imports to use tinytorch.core.* instead of tensor_dev
- Fixed tinytorch/core/losses.py with all loss functions
- Fixed tinytorch/core/training.py imports
ISSUE: Training loop runs but doesn't learn (gradients not flowing)
- Loss stays constant at 0.7911
- Weights don't update
- Likely autograd (Module 05) backward() not fully implemented
- Need to fix Tensor.backward() and gradient computation
This commit includes:
- Exported tinytorch package files from nbdev (autograd, losses, optimizers, training, etc.)
- Updated activations.py and layers.py with __call__ methods
- New module exports: attention, spatial, tokenization, transformer, etc.
- Removed old _modidx.py file
- Cleanup of duplicate milestone directories
These are the generated package files that correspond to the source modules
we've been developing. Students will import from these when using TinyTorch.
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)
- 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
✅ Fixed all forward dependency violations across modules 3-10
✅ Learning progression now clean: each module uses only previous concepts
Module 3 Activations:
- Removed 25+ autograd/Variable references
- Pure tensor-based activation functions
- Students learn nonlinearity without gradient complexity
Module 4 Layers:
- Removed 15+ autograd references
- Simplified Dense/Linear layers to pure tensor operations
- Clean building blocks without gradient tracking
Module 7 Spatial:
- Simplified 20+ autograd references to basic patterns
- Conv2D/BatchNorm work with basic gradients from Module 6
- Focus on CNN mechanics, not autograd complexity
Module 8 Optimizers:
- Simplified 50+ complex autograd references
- Basic SGD/Adam using simple gradient operations
- Educational focus on optimization math
Module 10 Training:
- Fixed import paths and simplified autograd usage
- Integration module using concepts from Modules 6-9 only
- Clean training loops without advanced patterns
RESULT: Clean learning progression where students only use concepts
they've already learned. No more circular dependencies!
Committing all remaining autograd and training improvements:
- Fixed autograd bias gradient aggregation
- Updated optimizers to preserve parameter shapes
- Enhanced loss functions with Variable support
- Added comprehensive gradient shape tests
This commit preserves the working state before cleaning up
the examples directory structure.
BREAKTHROUGH IMPLEMENTATION:
✅ Auto-generated warnings now added to ALL exported files automatically
✅ Clear source file paths shown in every tinytorch/ file header
✅ CLAUDE.md updated with crystal clear rules: tinytorch/ = edit modules/
✅ Export process now runs warnings BEFORE success message
SYSTEMATIC PREVENTION:
- Every exported file shows: AUTOGENERATED! DO NOT EDIT! File to edit: [source]
- THIS FILE IS AUTO-GENERATED FROM SOURCE MODULES - CHANGES WILL BE LOST!
- To modify this code, edit the source file listed above and run: tito module complete
WORKFLOW ENFORCEMENT:
- Golden rule established: If file path contains tinytorch/, DON'T EDIT IT DIRECTLY
- Automatic detection of 16 module mappings from tinytorch/ back to modules/source/
- Post-export processing ensures no exported file lacks protection warning
VALIDATION:
✅ Tested with multiple module exports - warnings added correctly
✅ All tinytorch/core/ files now protected with clear instructions
✅ Source file paths correctly mapped and displayed
This prevents ALL future source/compiled mismatch issues systematically.
CRITICAL FIXES:
- Fixed Adam & SGD optimizers corrupting parameter shapes with variable batch sizes
- Root cause: param.data = Tensor() created new tensor with wrong shape
- Solution: Use param.data._data[:] = ... to preserve original shape
CLAUDE.md UPDATES:
- Added CRITICAL RULE: Never modify core files directly
- Established mandatory workflow: Edit source → Export → Test
- Clear consequences for violations to prevent source/compiled mismatch
TECHNICAL DETAILS:
- Source fix in modules/source/10_optimizers/optimizers_dev.py
- Temporary fix in tinytorch/core/optimizers.py (needs proper export)
- Preserves parameter shapes across all batch sizes
- Enables variable batch size training without broadcasting errors
VALIDATION:
- Created comprehensive test suite validating shape preservation
- All optimizer tests pass with arbitrary batch sizes
- Ready for CIFAR-10 training with variable batches
- Regenerate all .ipynb files from fixed .py modules
- Update tinytorch package exports with corrected implementations
- Sync package module index with current 16-module structure
These generated files reflect all the module fixes and ensure consistent
.py ↔ .ipynb conversion with the updated module implementations.
- Exported 09_training module using nbdev directly from Python file
- Exported 08_optimizers module to resolve import dependencies
- All training components now available in tinytorch.core.training:
* MeanSquaredError, CrossEntropyLoss, BinaryCrossEntropyLoss
* Accuracy metric
* Trainer class with complete training orchestration
- All optimizers now available in tinytorch.core.optimizers:
* SGD, Adam optimizers
* StepLR learning rate scheduler
- All components properly exported and functional
- Integration tests passing (17/17)
- Inline tests passing (6/6)
- tito CLI integration working correctly
Package exports:
- tinytorch.core.training: 688 lines, 5 main classes
- tinytorch.core.optimizers: 17,396 bytes, complete optimizer suite
- Clean separation of development vs package code
- Ready for production use and further development
