mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-03 06:46:49 -05:00
9129935d5b
New Features: - Add MSEBackward gradient computation for regression tasks - Patch MSELoss in enable_autograd() for gradient tracking - All 3 loss functions now support autograd: MSE, BCE, CrossEntropy Test Suite Organization: - Reorganize tests/ into focused directories - Create tests/integration/ for cross-module tests - Create tests/05_autograd/ for autograd edge cases - Create tests/debugging/ for common student pitfalls - Add comprehensive tests/README.md explaining test philosophy Integration Tests: - Move test_gradient_flow.py to integration/ - 20 comprehensive gradient flow tests - Tests cover: tensors, layers, activations, losses, optimizers - Tests validate: basic ops, chain rule, broadcasting, training loops - 19/20 tests passing (MSE now fixed!) Results: ✅ Perceptron learns: 50% → 93% accuracy ✅ Clean test organization guides future development ✅ Tests catch the exact bugs that broke training Pedagogical Value: - Test organization teaches testing best practices - Gradient flow tests show what integration testing catches - Sets foundation for debugging/diagnostic tests
99 lines
3.2 KiB
Markdown
99 lines
3.2 KiB
Markdown
# TinyTorch Test Suite
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Comprehensive testing organized by purpose and scope.
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## Test Organization
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### 📦 Module Tests (`XX_modulename/`)
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**Purpose**: Test individual module functionality
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**Scope**: Single module, isolated behavior
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**Example**: `01_tensor/test_progressive_integration.py`
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These tests validate that each module works correctly in isolation.
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### 🔗 Integration Tests (`integration/`)
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**Purpose**: Test cross-module interactions
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**Scope**: Multiple modules working together
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**Files**:
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- `test_gradient_flow.py` - **CRITICAL**: Validates gradients flow through entire training stack
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- `test_end_to_end_training.py` - Full training loops (TODO)
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- `test_module_compatibility.py` - Module interfaces (TODO)
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**Why this matters**:
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- Catches bugs that unit tests miss
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- Validates the "seams" between modules
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- Ensures training actually works end-to-end
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### 🐛 Debugging Tests (`debugging/`)
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**Purpose**: Catch common student pitfalls
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**Scope**: Pedagogical - teaches debugging
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**Files**:
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- `test_gradient_vanishing.py` - Detect/diagnose vanishing gradients (TODO)
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- `test_gradient_explosion.py` - Detect/diagnose exploding gradients (TODO)
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- `test_common_mistakes.py` - "Did you forget backward()?" style tests (TODO)
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**Philosophy**: When these tests fail, the error message should teach the student what went wrong and how to fix it.
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### ⚡ Autograd Edge Cases (`05_autograd/`)
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**Purpose**: Stress-test autograd system
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**Scope**: Autograd internals and edge cases
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**Files**:
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- `test_broadcasting.py` - Broadcasting gradient bugs (TODO)
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- `test_computation_graph.py` - Graph construction edge cases (TODO)
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- `test_backward_edge_cases.py` - Numerical stability, etc. (TODO)
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## Running Tests
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### All tests
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```bash
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pytest tests/ -v
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```
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### Integration tests only (recommended for debugging training issues)
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```bash
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pytest tests/integration/ -v
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```
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### Specific test
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```bash
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pytest tests/integration/test_gradient_flow.py -v
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```
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### Run without pytest
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```bash
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python tests/integration/test_gradient_flow.py
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```
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## Test Philosophy
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1. **Integration tests catch real bugs**: The gradient flow test caught the exact bugs that prevented training
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2. **Descriptive names**: Test names should explain what they test
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3. **Good error messages**: When tests fail, students should understand why
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4. **Pedagogical value**: Tests teach correct usage patterns
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## Adding New Tests
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When adding a test, ask:
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- **Is it testing one module?** → Put in `XX_modulename/`
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- **Is it testing modules working together?** → Put in `integration/`
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- **Is it teaching debugging?** → Put in `debugging/`
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- **Is it an autograd edge case?** → Put in `05_autograd/`
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## Most Important Tests
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🔥 **Must pass before merging**:
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- `integration/test_gradient_flow.py` - If this fails, training is broken
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📚 **Module validation**:
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- Each module's inline tests (in `modules/source/`)
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- Module-specific tests in `tests/XX_modulename/`
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## Test Coverage Goals
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- ✅ All tensor operations have gradient tests
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- ✅ All layers compute gradients correctly
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- ✅ All activations integrate with autograd
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- ✅ All loss functions compute gradients
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- ✅ All optimizers update parameters
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- ⏳ End-to-end training converges (TODO)
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- ⏳ Common pitfalls are detected (TODO) |