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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.
🧪 TinyTorch Integration Tests
⚠️ CRITICAL DIRECTORY - DO NOT DELETE
This directory contains 17 integration test files that verify cross-module functionality across the entire TinyTorch system. These tests represent significant development effort and are essential for:
- Module integration validation
- Cross-component compatibility
- Real-world ML pipeline testing
- System-level regression detection
📁 Test Structure
test_*_integration.py- Cross-module integration teststest_utils.py- Shared testing utilitiestest_integration_report.md- Test documentation
🧪 Integration Test Coverage
Foundation Integration
test_tensor_activations_integration.py- Tensor + Activationstest_layers_networks_integration.py- Layers + Dense Networkstest_tensor_autograd_integration.py- Tensor + Autograd
Architecture Integration
test_tensor_attention_integration.py- NEW: Tensor + Attention mechanismstest_attention_pipeline_integration.py- NEW: Complete transformer-like pipelinestest_tensor_cnn_integration.py- Tensor + Spatial/CNNtest_cnn_networks_integration.py- Spatial + Dense Networkstest_cnn_pipeline_integration.py- Complete CNN pipelines
Training & Data Integration
test_dataloader_tensor_integration.py- DataLoader + Tensortest_training_integration.py- Complete training workflowstest_ml_pipeline_integration.py- End-to-end ML pipelines
Inference Serving Integration
test_compression_integration.py- Model compressiontest_kernels_integration.py- Custom operationstest_benchmarking_integration.py- Performance measurementtest_mlops_integration.py- Deployment and serving
🔧 Usage
# Run all integration tests
pytest tests/ -v
# Run specific module integration
pytest tests/test_tensor_attention_integration.py -v
pytest tests/test_attention_pipeline_integration.py -v
# Run attention-related tests
pytest tests/ -k "attention" -v
🚨 Recovery Instructions
If accidentally deleted:
git checkout HEAD -- tests/
git status # Verify recovery
📊 Test Coverage
These integration tests complement the inline tests in each module's *_dev.py files, providing comprehensive system validation with focus on:
- Real component integration (not mocks)
- Cross-module compatibility
- Realistic ML workflows (classification, seq2seq, transformers)
- Performance and scalability