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Milestone 02 Structure (matches milestone 01): - README.md: Comprehensive guide with historical context - xor_crisis.py: Part 1 - demonstrates single-layer failure (executable) - xor_solved.py: Part 2 - demonstrates multi-layer success (executable) Cleanup: - ✅ Removed old perceptron_xor_fails.py - ✅ Moved test files to tests/integration/ - test_xor_simple.py - test_xor_thorough.py - test_xor_original_1986.py (verifies 2-2-1 architecture works!) - ✅ Updated README with clear instructions - ✅ Made scripts executable Milestone 02 now has the same polish and structure as milestone 01: - Clear file naming (crisis vs solved) - Beautiful rich output - Historical context - Pedagogically structured
TinyTorch Test Suite
Comprehensive testing organized by purpose and scope.
Test Organization
📦 Module Tests (XX_modulename/)
Purpose: Test individual module functionality
Scope: Single module, isolated behavior
Example: 01_tensor/test_progressive_integration.py
These tests validate that each module works correctly in isolation.
🔗 Integration Tests (integration/)
Purpose: Test cross-module interactions
Scope: Multiple modules working together
Files:
test_gradient_flow.py- CRITICAL: Validates gradients flow through entire training stacktest_end_to_end_training.py- Full training loops (TODO)test_module_compatibility.py- Module interfaces (TODO)
Why this matters:
- Catches bugs that unit tests miss
- Validates the "seams" between modules
- Ensures training actually works end-to-end
🐛 Debugging Tests (debugging/)
Purpose: Catch common student pitfalls
Scope: Pedagogical - teaches debugging
Files:
test_gradient_vanishing.py- Detect/diagnose vanishing gradients (TODO)test_gradient_explosion.py- Detect/diagnose exploding gradients (TODO)test_common_mistakes.py- "Did you forget backward()?" style tests (TODO)
Philosophy: When these tests fail, the error message should teach the student what went wrong and how to fix it.
⚡ Autograd Edge Cases (05_autograd/)
Purpose: Stress-test autograd system
Scope: Autograd internals and edge cases
Files:
test_broadcasting.py- Broadcasting gradient bugs (TODO)test_computation_graph.py- Graph construction edge cases (TODO)test_backward_edge_cases.py- Numerical stability, etc. (TODO)
Running Tests
All tests
pytest tests/ -v
Integration tests only (recommended for debugging training issues)
pytest tests/integration/ -v
Specific test
pytest tests/integration/test_gradient_flow.py -v
Run without pytest
python tests/integration/test_gradient_flow.py
Test Philosophy
- Integration tests catch real bugs: The gradient flow test caught the exact bugs that prevented training
- Descriptive names: Test names should explain what they test
- Good error messages: When tests fail, students should understand why
- Pedagogical value: Tests teach correct usage patterns
Adding New Tests
When adding a test, ask:
- Is it testing one module? → Put in
XX_modulename/ - Is it testing modules working together? → Put in
integration/ - Is it teaching debugging? → Put in
debugging/ - Is it an autograd edge case? → Put in
05_autograd/
Most Important Tests
🔥 Must pass before merging:
integration/test_gradient_flow.py- If this fails, training is broken
📚 Module validation:
- Each module's inline tests (in
modules/source/) - Module-specific tests in
tests/XX_modulename/
Test Coverage Goals
- ✅ All tensor operations have gradient tests
- ✅ All layers compute gradients correctly
- ✅ All activations integrate with autograd
- ✅ All loss functions compute gradients
- ✅ All optimizers update parameters
- ⏳ End-to-end training converges (TODO)
- ⏳ Common pitfalls are detected (TODO)