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Test Cleanup (113 files, -22,000 lines): - Remove 21 redundant run_all_tests.py files - Remove checkpoints/ folder (22 obsolete checkpoint files) - Remove progressive/, debugging/, diagnostic/ folders - Remove duplicate integration tests and examples - Remove orphaned dev artifacts and generated outputs - Consolidate test_gradient_flow_overall.py into system/ Documentation Cleanup (4 files removed): - Remove duplicate HOW_TO_USE.md, WORKFLOW.md, SYSTEM_DESIGN.md - Trim environment/README.md from 334 to 86 lines - Update capstone/README.md removing outdated bug references Test Fixes: - Add requires_grad=True to layer parameters in gradient tests - Fix PositionalEncoding argument order in test_shapes.py - Adjust performance thresholds for realistic expectations - Fix gradient clipping to handle memoryview correctly - Update zero_grad assertions to accept None or zeros
73 lines
2.5 KiB
Markdown
73 lines
2.5 KiB
Markdown
# Capstone Integration Tests - Module 20
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Comprehensive integration tests that validate the ENTIRE TinyTorch learning journey.
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## Overview
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The capstone tests verify that all 19 previous modules work together to build production-ready ML systems.
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## Test Coverage
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### Priority 1: Complete ML Pipeline
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- **test_complete_ml_pipeline_end_to_end**: Full data → model → training → evaluation
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- Validates: Modules 01-08 integration
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### Priority 2: Model Architecture
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- **test_mlp_architecture_integration**: Multi-layer perceptron
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- **test_cnn_architecture_integration**: CNN with Conv2d, pooling, flatten
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- **test_transformer_architecture_integration**: Attention, embeddings, positional encoding
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### Priority 3: Training Convergence
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- **test_xor_convergence**: Classic XOR problem
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- **test_binary_classification_convergence**: Real binary classification
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### Priority 4: Optimization & Deployment
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- **test_quantization_pipeline**: INT8 quantization
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- **test_pruning_pipeline**: Weight pruning
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- **test_combined_optimization_deployment**: Quantization + pruning together
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### Priority 5: Gradient Flow & Performance
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- **test_deep_network_gradient_flow**: Gradients through all layer types
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- **test_memory_efficiency**: Reasonable memory usage
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- **test_training_performance**: Training speed meets expectations
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## Running Tests
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```bash
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# Run all capstone tests
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pytest tests/20_capstone/ -v
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# Run specific test class
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pytest tests/20_capstone/test_capstone_core.py::TestCompleteMLPipeline -v
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```
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## Test Philosophy
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Tests follow production ML workflow patterns:
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1. **Data Creation** → Representative datasets
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2. **Model Building** → Real architectures (MLP, CNN, Transformer)
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3. **Training** → Actual convergence (loss decreases, accuracy improves)
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4. **Evaluation** → Real metrics
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5. **Optimization** → Production techniques (quantization, pruning)
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## Success Criteria
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For capstone tests to pass, students must have:
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1. Built all 19 modules correctly
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2. Integrated modules properly
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3. Implemented autograd correctly (gradients flow everywhere)
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4. Created working optimizers
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5. Validated on real tasks (models actually learn)
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## What This Tests That Unit Tests Don't
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| Aspect | Unit Tests | Capstone Tests |
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| Scope | Single module | All 19 modules together |
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| Integration | Module isolation | Cross-module integration |
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| Real workflows | Synthetic checks | Production ML pipelines |
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| Learning | Correctness only | Models must converge |
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| Deployment | Not tested | Quantization, pruning tested |
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