🧪 Testing Framework

Test-Driven ML Engineering

TinyTorch’s testing framework ensures your implementations are not just educational, but production-ready and reliable.

🎯 Testing Philosophy: Verify Understanding Through Implementation

TinyTorch testing goes beyond checking syntax - it validates that you understand ML systems engineering through working implementations.

⚡ Quick Start: Validate Your Implementation

🚀 Run Everything (Recommended)

# Complete validation suite
tito test --comprehensive

# Expected output:
# 🧪 Running 16 module tests...
# 🔗 Running integration tests...
# 📊 Running performance benchmarks...
# ✅ Overall TinyTorch Health: 100.0%

🎯 Target-Specific Testing

# Test what you just built
tito module complete 02_tensor && tito checkpoint test 01

# Quick module check
tito test --module attention --verbose

# Performance validation
tito test --performance --module training

🔬 Testing Levels: From Components to Systems

1. 🧩 Module-Level Testing

Goal: Verify individual components work correctly in isolation

# Test what you just implemented
tito test --module tensor --verbose
tito test --module attention --detailed

# Quick health check for specific module
tito module validate spatial

# Debug failing module
tito test --module autograd --debug

What Gets Tested:

• ✅ Core functionality (forward pass, backward pass)

• ✅ Memory usage patterns and leaks

• ✅ Mathematical correctness vs reference implementations

• ✅ Edge cases and error handling

2. 🔗 Integration Testing

Goal: Ensure modules work together seamlessly

# Test module dependencies
tito test --integration --focus training

# Validate export/import chain
tito test --exports --all-modules

# Full pipeline validation
tito test --pipeline --from tensor --to training

Integration Scenarios:

• Tensor → Autograd: Gradient flow works correctly

• Spatial → Training: CNN training pipeline functions end-to-end

• Attention → TinyGPT: Transformer components integrate properly

• All Modules: Complete framework functionality

3. 🏆 Checkpoint Testing

Goal: Validate you’ve achieved specific learning capabilities

# Test your current capabilities
tito checkpoint test 01  # "Can I create and manipulate tensors?"
tito checkpoint test 08  # "Can I train neural networks end-to-end?"
tito checkpoint test 13  # "Can I build attention mechanisms?"

# Progressive capability validation
tito checkpoint validate --from 00 --to 15

See Complete Checkpoint System Documentation →

Key Capability Categories:

• Foundation (00-03): Building blocks of neural networks

• Training (04-08): End-to-end learning systems

• Architecture (09-14): Advanced model architectures

• Optimization (15+): Production-ready systems

4. 📊 Performance & Systems Testing

Goal: Verify your implementation meets performance expectations

# Memory usage analysis
tito test --memory --module training --profile

# Speed benchmarking
tito test --speed --compare-baseline

# Scaling behavior validation
tito test --scaling --model-sizes 1M,5M,10M

Performance Metrics:

• Memory efficiency: Peak usage, gradient memory, batch scaling

• Training speed: Convergence time, throughput (samples/sec)

• Inference latency: Forward pass time, batch processing efficiency

• Scaling behavior: Performance vs model size, memory vs accuracy trade-offs

5. 🌍 Real-World Example Validation

Goal: Demonstrate production-ready functionality

# Train actual models
tito example train-mnist-mlp     # 95%+ accuracy target
tito example train-cifar-cnn     # 75%+ accuracy target  
tito example generate-text       # TinyGPT coherent generation

# Production scenarios
tito example benchmark-inference  # Speed/memory competitive analysis
tito example deploy-edge         # Resource-constrained deployment

🏗️ Test Architecture: Systems Engineering Approach

📋 Progressive Testing Pattern

Every TinyTorch module follows consistent testing standards:

# Module testing template (every module follows this pattern)
class ModuleTest:
    def test_core_functionality(self):     # Basic operations work
    def test_mathematical_correctness(self): # Matches reference implementations  
    def test_memory_usage(self):          # No memory leaks, efficient usage
    def test_integration_ready(self):     # Exports correctly for other modules
    def test_real_world_usage(self):      # Works in actual ML pipelines

📁 Test Organization Structure

tests/
├── checkpoints/                    # 16 capability validation tests
│   ├── checkpoint_00_environment.py   # Development setup working
│   ├── checkpoint_01_foundation.py    # Tensor operations mastered
│   └── checkpoint_15_capstone.py      # Complete ML systems expertise
├── integration/                    # Cross-module compatibility
│   ├── test_training_pipeline.py      # End-to-end training works
│   └── test_module_exports.py         # All modules export correctly  
├── performance/                    # Systems performance validation
│   ├── memory_profiling.py           # Memory usage analysis
│   └── speed_benchmarks.py           # Computational performance
└── examples/                      # Real-world usage validation
    ├── test_mnist_training.py         # Actual MNIST training works
    └── test_cifar_cnn.py             # CNN achieves 75%+ on CIFAR-10

📊 Understanding Test Results

🎯 Health Status Interpretation

Score	Status	Action Required
100%	🟢 Excellent	All systems operational, ready for production
95-99%	🟡 Good	Minor issues, investigate warnings
90-94%	🟠 Caution	Some failing tests, address specific modules
<90%	🔴 Issues	Significant problems, requires immediate attention

🚦 Module Status Indicators

• ✅ Passing: Module implemented correctly, all tests green

• ⚠️ Warning: Minor issues detected, functionality mostly intact

• ❌ Failing: Critical errors, module needs debugging

• 🚧 In Progress: Module under development, tests expected to fail

• 🎯 Checkpoint Ready: Module ready for capability testing

💡 Best Practices: Test-Driven ML Engineering

🔄 During Active Development

# Continuous validation workflow
tito test --module tensor         # After implementing core functionality
tito test --integration tensor    # After module completion  
tito checkpoint test 01          # After achieving milestone

Development Testing Pattern:

1. Write minimal test first: Define expected behavior before implementation

2. Test each component: Validate individual functions as you build them

3. Integration early: Test module interactions frequently, not just at the end

4. Performance check: Monitor memory and speed throughout development

✅ Before Code Commits

# Pre-commit validation checklist
tito test --comprehensive        # Full test suite passes
tito system doctor              # Environment is healthy
tito checkpoint status          # All achieved capabilities still work

Commit Readiness Criteria:

• ✅ All tests pass (100% health status)

• ✅ No memory leaks detected in performance tests

• ✅ Integration tests confirm module exports work

• ✅ Checkpoint tests validate learning objectives met

🎯 Before Module Completion

# Module completion validation
tito test --module mymodule --comprehensive
tito test --integration --focus mymodule  
tito module validate mymodule
tito module complete mymodule    # Only after all tests pass

🔧 Troubleshooting Guide

🚨 Common Test Failures & Solutions

Module Import Errors

# Problem: Module won't import
❌ ModuleNotFoundError: No module named 'tinytorch.core.tensor'

# Solution: Check module export
tito module complete tensor      # Ensure module is properly exported
tito system doctor             # Verify Python path and virtual environment

Mathematical Correctness Failures

# Problem: Your implementation doesn't match reference
❌ AssertionError: Expected 0.5, got 0.48 (tolerance: 0.01)

# Debug process:
tito test --module tensor --debug          # Get detailed failure info
python -c "import tinytorch; help(tinytorch.tensor)"  # Check implementation

Memory Usage Issues

# Problem: Memory tests failing
❌ Memory usage: 150MB (expected: <100MB)

# Investigation:
tito test --memory --profile tensor       # Get memory profile
tito test --scaling --module tensor       # Check scaling behavior

Integration Test Failures

# Problem: Modules don't work together
❌ Integration test: tensor→autograd failed

# Debugging approach:
tito test --integration --focus autograd --verbose
tito test --exports tensor                # Check tensor exports correctly
tito test --imports autograd             # Check autograd imports correctly

🔍 Advanced Debugging Techniques

Verbose Test Output

# Get detailed test information
tito test --module attention --verbose --debug

# See exact error locations
tito test --traceback --module training

Performance Profiling

# Memory usage analysis
tito test --memory --profile --module spatial

# Speed profiling  
tito test --speed --profile --module training --iterations 100

Environment Validation

# Complete environment check
tito system doctor --comprehensive

# Specific dependency verification
tito system check-dependencies --module autograd

📋 Test Failure Decision Tree

Test Failed?
├── Import Error?
│   ├── Run `tito system doctor`
│   └── Check virtual environment activation
├── Mathematical Error?
│   ├── Compare with reference implementation
│   └── Check tensor shapes and dtypes
├── Memory Error? 
│   ├── Profile memory usage patterns
│   └── Check for memory leaks in loops
├── Integration Error?
│   ├── Test modules individually first
│   └── Verify export/import chain
└── Performance Error?
    ├── Profile bottlenecks
    └── Check algorithmic complexity

🎯 Testing Philosophy: Building Reliable ML Systems

The TinyTorch testing framework embodies professional ML engineering principles:

🧩 KISS Principle in Testing

• Consistent patterns: Every module follows identical testing structure - learn once, apply everywhere

• Actionable feedback: Tests provide specific error messages with exact fix suggestions

• Essential focus: Tests validate critical functionality without unnecessary complexity

🔗 Systems Engineering Mindset

• Integration-first: Tests verify components work together, not just in isolation

• Real-world validation: Examples prove your code works on actual datasets (CIFAR-10, MNIST)

• Performance consciousness: All tests include memory and speed awareness

📚 Educational Excellence

• Understanding verification: Tests confirm you grasp concepts, not just syntax

• Progressive mastery: Capabilities build systematically through checkpoint validation

• Immediate feedback: Know instantly if your implementation meets professional standards

🚀 Production Readiness

• Professional standards: Tests match industry-level validation practices

• Scalability validation: Ensure your code works at realistic data sizes

• Reliability assurance: Comprehensive testing prevents production failures

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🏆 Success Metrics

Testing Success

A well-tested TinyTorch implementation should achieve:

• 100% test suite passing - All functionality works correctly

• >95% memory efficiency - Comparable to reference implementations

• Real dataset success - MNIST 95%+, CIFAR-10 75%+ accuracy targets

• Clean integration - All modules work together seamlessly

Remember: TinyTorch testing doesn’t just verify your code works - it confirms you understand ML systems engineering well enough to build production-ready implementations.

Your testing discipline here translates directly to building reliable ML systems in industry settings!

🚀 Next Steps

Ready to start testing your implementations?

# Begin with comprehensive health check
tito test --comprehensive

# Start building and testing your first module
tito module complete 01_setup

# Track your testing progress
tito checkpoint status

Testing Integration with Your Learning Path:

• Track Your Progress → - See how testing fits into capability development

• Track Capabilities → - Use automated testing for progress validation

• Showcase Achievements → - Testing validates the skills you can claim

🎯 Testing Excellence = ML Systems Mastery

Every test you write and run builds the discipline needed for production ML engineering