TinyTorch/docs/development/testing-separation.md

Testing and Status Separation in TinyTorch

Overview

TinyTorch now has two separate systems for checking different aspects of the project:

1. Module Status Checking - Are the individual modules working and passing their tests?
2. TinyTorch Package Checking - Are the core capabilities available in the exported package?

1. Module Status Checking: tito modules

Purpose: Check the development status of individual modules in the modules/ directory.

What it checks:

• ✅ File Structure: Does the module have required files ({module}_dev.py, tests/test_{module}.py, README.md)?
• ✅ Test Results: Do the module's tests pass when run with pytest?
• ✅ Development Progress: Which modules are complete vs. incomplete?

Commands:

# Basic module status overview
tito modules

# Run tests for all modules and show results
tito modules --test

# Show detailed file structure breakdown
tito modules --details

Example Output:

Module Status Overview
┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Module          ┃   Dev File   ┃    Tests     ┃    README    ┃     Test Results     ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ activations     │      ✅      │      ✅      │      ✅      │      ✅ Passed       │
│ tensor          │      ✅      │      ✅      │      ✅      │      ❌ Failed       │
│ autograd        │      ❌      │      ❌      │      ❌      │       No tests       │
└─────────────────┴──────────────┴──────────────┴──────────────┴──────────────────────┘

📊 Summary: 7/16 modules complete, 4/16 tests passing

2. TinyTorch Package Checking: tito info

Purpose: Check if the exported TinyTorch package has the required functionality available.

What it checks:

• ✅ Package Capabilities: Can you import and use core TinyTorch functionality?
• ✅ Functional Integration: Do the different components work together?
• ✅ Student Experience: What can students actually use from the package?

Commands:

# Check TinyTorch package functionality
tito info

# Show hello message if setup is working
tito info --hello

Example Output:

🚀 Module Implementation Status
┏━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ID  ┃ Project      ┃       Status       ┃ Description                              ┃
┡━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│  1  │ Tensor       │   ✅ Implemented   │ basic tensor operations                  │
│  2  │ Layers       │   ✅ Implemented   │ neural network building blocks           │
│  6  │ DataLoader   │   ✅ Implemented   │ data loading pipeline                    │
│  7  │ Training     │   ⏳ Not Started   │ autograd engine & optimization           │
└─────┴──────────────┴────────────────────┴──────────────────────────────────────────┘

Key Differences

Module Status (tito modules)

• Scope: Individual module development files
• Focus: Development workflow and testing
• Checks: File structure, test execution, completion status
• Audience: Developers working on modules
• Granularity: Per-module basis

Package Status (tito info)

• Scope: Exported TinyTorch package functionality
• Focus: Student experience and package capabilities
• Checks: Import capabilities, functional integration
• Audience: Students using the package
• Granularity: Functional capabilities

Use Cases

For Module Developers

# Check which modules need work
tito modules

# Test all modules during development
tito modules --test

# See detailed breakdown of missing files
tito modules --details

For Students/Users

# Check what TinyTorch features are available
tito info

# Test that my environment is working
tito info --hello

For Instructors

# Check overall course progress
tito modules --test

# Verify student experience
tito info

# Run specific module tests
tito test --module tensor

Architecture Benefits

✅ Clear Separation of Concerns

• Module development workflow vs. package functionality
• Different audiences, different needs
• No confusion between "learning modules" and "package capabilities"

✅ Accurate Status Reporting

• Module status reflects development progress
• Package status reflects student experience
• No false positives from organizational mismatches

✅ Educational Value

• Students see what they can actually use
• Developers see what needs to be implemented
• Clear progression from module → package → student usage

Implementation Notes

Module Status Implementation

• Scans modules/ directory automatically
• Checks for required files: {module}_dev.py, tests/test_{module}.py, README.md
• Runs pytest on test files when --test flag is used
• Provides detailed file structure breakdown with --details

Package Status Implementation

• Tests actual import capabilities from tinytorch package
• Checks functional integration (e.g., can you create a tensor and use it?)
• Focuses on capabilities not module organization
• Maps learning modules to functional components

Future Enhancements

Module Status

• Add git status integration
• Show module dependencies
• Track completion percentages
• Add module-specific metrics

Package Status

• Add performance benchmarks
• Show version compatibility
• Add integration test results
• Track API completeness

This separation provides a much clearer picture of both development progress and student experience, avoiding the confusion that arose from trying to map pedagogical structure directly to package organization.