mirror of https://github.com/MLSysBook/TinyTorch.git synced 2026-03-25 21:19:42 -05:00

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Vijay Janapa Reddi e779d67dcf feat: Add comprehensive integration tests for attention module

✅ 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

2025-07-18 00:21:48 -04:00

2.5 KiB

Raw Blame History

🧪 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 tests
test_utils.py - Shared testing utilities
test_integration_report.md - Test documentation

🧪 Integration Test Coverage

Foundation Integration

test_tensor_activations_integration.py - Tensor + Activations
test_layers_networks_integration.py - Layers + Dense Networks
test_tensor_autograd_integration.py - Tensor + Autograd

Architecture Integration

test_tensor_attention_integration.py - NEW: Tensor + Attention mechanisms
test_attention_pipeline_integration.py - NEW: Complete transformer-like pipelines
test_tensor_cnn_integration.py - Tensor + Spatial/CNN
test_cnn_networks_integration.py - Spatial + Dense Networks
test_cnn_pipeline_integration.py - Complete CNN pipelines

Training & Data Integration

test_dataloader_tensor_integration.py - DataLoader + Tensor
test_training_integration.py - Complete training workflows
test_ml_pipeline_integration.py - End-to-end ML pipelines

Inference Serving Integration

test_compression_integration.py - Model compression
test_kernels_integration.py - Custom operations
test_benchmarking_integration.py - Performance measurement
test_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

2.5 KiB Raw Blame History