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5c20608776
✅ Refactored test_tensor_activations_integration.py: - Changed from re-testing activation math to testing Tensor-Activation interfaces - Focus on: Tensor input → Activation → Tensor output compatibility - Test dtype preservation, shape preservation, chaining, error handling - Test activation outputs work with further Tensor operations ✅ Refactored test_layers_networks_integration.py: - Changed from re-testing layer/network logic to testing Layer-Dense interfaces - Focus on: Dense layer → Sequential network → MLP composition - Test layer output as network input, network output as layer input - Test multi-stage pipelines, parallel processing, modular replacement Integration tests now properly focus on: ✅ Cross-module interface compatibility (not individual functionality) ✅ Data flow and pipeline integration between modules ✅ Shape/dtype preservation across module boundaries ✅ System-level workflows and architectural patterns ✅ Error handling when modules are incompatibly connected ✅ Component modularity and interchangeability Establishes proper integration testing philosophy: test that modules work TOGETHER, not what individual modules do (that's for inline tests).
🧪 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