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✅ Created test_tensor_attention_integration.py: - Basic tensor-attention integration with real TinyTorch components - Self-attention wrapper testing with proper Tensor objects - Attention masking integration (causal, padding, bidirectional) - Batched tensor processing and different data types - Numerical stability and gradient flow compatibility ✅ Created test_attention_pipeline_integration.py: - Complete transformer-like pipeline testing - Multi-layer attention stacks (transformer encoders) - Causal masking for language modeling workflows - Encoder-decoder architecture integration - Cross-module integration with dense layers and activations - Real-world scenarios: sequence classification, seq2seq translation - Scalability testing across different sequence lengths and dimensions ✅ Updated tests/README.md: - Documented new attention integration tests (15→17 total tests) - Organized tests by category (Foundation, Architecture, Training, Inference Serving) - Added specific usage examples for attention tests - Clear documentation of test coverage and purpose Integration tests ensure: - Attention works with real Tensor objects (not mocks) - Cross-module compatibility with dense, spatial, activations - Complete ML workflows (classification, translation, transformers) - Realistic transformer architectures and patterns - System-level regression detection for attention functionality
67 lines
2.5 KiB
Markdown
67 lines
2.5 KiB
Markdown
# 🧪 TinyTorch Integration Tests
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## ⚠️ **CRITICAL DIRECTORY - DO NOT DELETE**
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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:
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- **Module integration validation**
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- **Cross-component compatibility**
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- **Real-world ML pipeline testing**
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- **System-level regression detection**
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## 📁 **Test Structure**
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- `test_*_integration.py` - Cross-module integration tests
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- `test_utils.py` - Shared testing utilities
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- `test_integration_report.md` - Test documentation
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## 🧪 **Integration Test Coverage**
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### Foundation Integration
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- `test_tensor_activations_integration.py` - Tensor + Activations
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- `test_layers_networks_integration.py` - Layers + Dense Networks
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- `test_tensor_autograd_integration.py` - Tensor + Autograd
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### Architecture Integration
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- `test_tensor_attention_integration.py` - **NEW**: Tensor + Attention mechanisms
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- `test_attention_pipeline_integration.py` - **NEW**: Complete transformer-like pipelines
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- `test_tensor_cnn_integration.py` - Tensor + Spatial/CNN
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- `test_cnn_networks_integration.py` - Spatial + Dense Networks
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- `test_cnn_pipeline_integration.py` - Complete CNN pipelines
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### Training & Data Integration
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- `test_dataloader_tensor_integration.py` - DataLoader + Tensor
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- `test_training_integration.py` - Complete training workflows
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- `test_ml_pipeline_integration.py` - End-to-end ML pipelines
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### Inference Serving Integration
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- `test_compression_integration.py` - Model compression
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- `test_kernels_integration.py` - Custom operations
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- `test_benchmarking_integration.py` - Performance measurement
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- `test_mlops_integration.py` - Deployment and serving
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## 🔧 **Usage**
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```bash
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# Run all integration tests
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pytest tests/ -v
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# Run specific module integration
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pytest tests/test_tensor_attention_integration.py -v
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pytest tests/test_attention_pipeline_integration.py -v
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# Run attention-related tests
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pytest tests/ -k "attention" -v
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```
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## 🚨 **Recovery Instructions**
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If accidentally deleted:
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```bash
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git checkout HEAD -- tests/
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git status # Verify recovery
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```
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## 📊 **Test Coverage**
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These integration tests complement the inline tests in each module's `*_dev.py` files, providing comprehensive system validation with focus on:
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- **Real component integration** (not mocks)
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- **Cross-module compatibility**
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- **Realistic ML workflows** (classification, seq2seq, transformers)
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- **Performance and scalability** |