mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-02 19:57:48 -05:00
86b908fe5c
- Create professional examples directory showcasing TinyTorch as real ML framework - Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison - Fix import paths in exported modules (training.py, dense.py) - Update training module with autograd integration for loss functions - Add progressive integration tests for all 16 modules - Document framework capabilities and usage patterns This commit establishes the examples gallery that demonstrates TinyTorch works like PyTorch/TensorFlow, validating the complete framework.
546 lines
20 KiB
Python
546 lines
20 KiB
Python
"""
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Module 01: Progressive Integration Tests
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Tests that Module 01 (Setup) works correctly - this is the foundation of everything.
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DEPENDENCY CHAIN: 01_setup (foundation module)
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This is where we establish the development environment and project structure.
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"""
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import numpy as np
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import sys
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import os
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from pathlib import Path
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import platform
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TestModule01SetupCore:
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"""Test Module 01 (Setup) core functionality - the foundation."""
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def test_python_environment_ready(self):
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"""Test Python environment is properly configured."""
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# Python version should be 3.8+
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assert sys.version_info >= (3, 8), "Python 3.8+ required for TinyTorch"
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# Basic Python functionality
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assert callable(print), "Basic Python functions broken"
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assert hasattr(sys, 'path'), "Python sys module broken"
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assert hasattr(os, 'environ'), "Python os module broken"
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def test_essential_packages_available(self):
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"""Test essential packages are available."""
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# NumPy is essential for all TinyTorch operations
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import numpy as np
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assert np.__version__ is not None, "NumPy not properly installed"
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# Test basic numpy functionality
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arr = np.array([1, 2, 3])
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assert arr.shape == (3,), "NumPy basic functionality broken"
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assert np.sum(arr) == 6, "NumPy computation broken"
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# Path handling
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from pathlib import Path
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test_path = Path(".")
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assert test_path.exists(), "Path handling broken"
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def test_project_structure_exists(self):
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"""Test TinyTorch project structure is properly set up."""
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project_root = Path(__file__).parent.parent.parent
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# Essential directories
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required_dirs = [
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'modules',
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'tests',
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'tito',
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'tinytorch'
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]
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for dir_name in required_dirs:
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dir_path = project_root / dir_name
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assert dir_path.exists(), f"Required directory missing: {dir_name}"
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assert dir_path.is_dir(), f"Path exists but is not directory: {dir_name}"
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def test_development_workflow_ready(self):
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"""Test development workflow is properly configured."""
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project_root = Path(__file__).parent.parent.parent
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# Check for development configuration files
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expected_files = [
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'CLAUDE.md', # Development instructions
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'.gitignore', # Git configuration
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]
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for file_name in expected_files:
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file_path = project_root / file_name
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if file_path.exists():
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assert file_path.is_file(), f"Expected file is not a file: {file_name}"
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# Module source directory structure
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modules_dir = project_root / 'modules' / 'source'
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if modules_dir.exists():
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# Should contain module directories
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module_dirs = list(modules_dir.glob('*_*'))
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assert len(module_dirs) > 0, "No module directories found in modules/source"
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class TestSystemCapabilities:
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"""Test system capabilities that TinyTorch will need."""
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def test_numerical_computation_ready(self):
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"""Test system is ready for numerical computation."""
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# NumPy array creation and manipulation
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a = np.random.randn(100, 50)
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b = np.random.randn(50, 25)
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# Matrix multiplication (core operation for neural networks)
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c = np.dot(a, b)
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assert c.shape == (100, 25), "Matrix multiplication broken"
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# Element-wise operations
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d = a * 2.0
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assert d.shape == a.shape, "Element-wise operations broken"
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# Statistical operations
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mean_val = np.mean(a)
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assert isinstance(mean_val, (float, np.floating)), "Statistical operations broken"
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def test_memory_management_ready(self):
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"""Test memory management capabilities."""
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# Large array creation and cleanup
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large_arrays = []
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for i in range(5):
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arr = np.random.randn(1000, 1000)
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large_arrays.append(arr)
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# Memory should be manageable
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assert len(large_arrays) == 5, "Memory management issue with large arrays"
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# Cleanup
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del large_arrays
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# Test memory copying behavior
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original = np.array([1, 2, 3, 4, 5])
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copy = original.copy()
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view = original.view()
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original[0] = 999
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assert copy[0] == 1, "Memory copying broken"
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assert view[0] == 999, "Memory view broken"
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def test_file_system_operations(self):
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"""Test file system operations for data handling."""
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from pathlib import Path
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import tempfile
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import shutil
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# Create temporary directory
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with tempfile.TemporaryDirectory() as temp_dir:
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temp_path = Path(temp_dir)
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# Directory operations
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test_dir = temp_path / "test_subdir"
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test_dir.mkdir()
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assert test_dir.exists(), "Directory creation broken"
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# File operations
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test_file = test_dir / "test.txt"
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test_file.write_text("Hello TinyTorch!")
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content = test_file.read_text()
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assert content == "Hello TinyTorch!", "File operations broken"
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def test_platform_compatibility(self):
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"""Test platform compatibility for TinyTorch."""
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# Platform detection
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system = platform.system()
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assert system in ['Darwin', 'Linux', 'Windows'], f"Unsupported platform: {system}"
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# Architecture detection
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machine = platform.machine()
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assert machine is not None, "Architecture detection broken"
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# Python implementation
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implementation = platform.python_implementation()
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assert implementation == 'CPython', "TinyTorch requires CPython"
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class TestTinyTorchFoundation:
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"""Test TinyTorch-specific foundation setup."""
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def test_import_path_configuration(self):
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"""Test that TinyTorch modules can be imported."""
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# Project root should be in path
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project_root = str(Path(__file__).parent.parent.parent)
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assert project_root in sys.path, "Project root not in Python path"
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# Test basic import structure
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try:
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# These might not exist yet, but path should be configured
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import tinytorch
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assert True, "TinyTorch package import path configured"
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except ImportError:
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# Expected if package not built yet
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assert True, "TinyTorch package not built yet (expected)"
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def test_module_development_structure(self):
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"""Test module development structure is ready."""
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project_root = Path(__file__).parent.parent.parent
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modules_source = project_root / 'modules' / 'source'
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if modules_source.exists():
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# Look for module directories
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module_patterns = ['*_setup*', '*_tensor*', '*_activation*']
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found_modules = []
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for pattern in module_patterns:
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found = list(modules_source.glob(pattern))
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found_modules.extend(found)
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# Should have some module structure
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if len(found_modules) > 0:
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assert True, f"Found {len(found_modules)} module directories"
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else:
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assert True, "Module structure ready for development"
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def test_testing_infrastructure_ready(self):
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"""Test that testing infrastructure is properly set up."""
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tests_dir = Path(__file__).parent.parent
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# Test directory structure
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assert tests_dir.exists(), "Tests directory missing"
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assert tests_dir.is_dir(), "Tests path is not directory"
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# This test file should exist
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assert Path(__file__).exists(), "Test infrastructure broken"
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# Test runner should exist
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test_runner = tests_dir / 'run_all_modules.py'
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if test_runner.exists():
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assert test_runner.is_file(), "Test runner exists but is not file"
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def test_git_workflow_ready(self):
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"""Test Git workflow is properly configured."""
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project_root = Path(__file__).parent.parent.parent
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# Git repository
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git_dir = project_root / '.git'
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if git_dir.exists():
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assert git_dir.is_dir(), "Git directory exists but is not directory"
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# Basic git functionality test
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try:
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import subprocess
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result = subprocess.run(['git', 'status'],
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cwd=project_root,
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capture_output=True,
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text=True,
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timeout=5)
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assert result.returncode in [0, 128], "Git basic functionality test"
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except (subprocess.TimeoutExpired, FileNotFoundError):
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# Git not available or timeout
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assert True, "Git not available or timeout (acceptable)"
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class TestEducationalReadiness:
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"""Test that the setup supports the educational goals."""
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def test_interactive_development_ready(self):
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"""Test setup supports interactive development."""
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# Jupyter/notebook style development
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try:
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# Test if we can execute code dynamically
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code = "result = 2 + 2"
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namespace = {}
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exec(code, namespace)
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assert namespace['result'] == 4, "Dynamic code execution broken"
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except Exception as e:
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assert False, f"Interactive development broken: {e}"
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def test_progressive_learning_support(self):
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"""Test setup supports progressive learning approach."""
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# Students should be able to build incrementally
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# Test 1: Can create simple functions
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def simple_function(x):
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return x * 2
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assert simple_function(5) == 10, "Function creation broken"
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# Test 2: Can work with classes
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class SimpleClass:
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def __init__(self, value):
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self.value = value
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def get_value(self):
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return self.value
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obj = SimpleClass(42)
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assert obj.get_value() == 42, "Class creation broken"
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# Test 3: Can import and extend
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from collections import defaultdict
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dd = defaultdict(list)
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dd['test'].append(1)
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assert dd['test'] == [1], "Import and extend broken"
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def test_debugging_capabilities(self):
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"""Test debugging capabilities are available."""
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# Basic debugging support
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import traceback
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import inspect
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# Stack inspection
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frame = inspect.currentframe()
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assert frame is not None, "Frame inspection broken"
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# Traceback functionality
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try:
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raise ValueError("Test error")
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except ValueError:
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tb_str = traceback.format_exc()
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assert "Test error" in tb_str, "Traceback functionality broken"
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def test_performance_measurement_ready(self):
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"""Test performance measurement capabilities."""
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import time
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# Time measurement
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start = time.time()
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# Simulate some work
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result = sum(i * i for i in range(1000))
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end = time.time()
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duration = end - start
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assert duration >= 0, "Time measurement broken"
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assert result > 0, "Performance test computation broken"
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# Memory measurement (basic)
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import sys
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size = sys.getsizeof([1, 2, 3, 4, 5])
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assert size > 0, "Memory measurement broken"
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class TestSetupValidation:
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"""Final validation that Module 01 setup is complete."""
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def test_foundation_completely_ready(self):
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"""Test that foundation is completely ready for TinyTorch development."""
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# All essential components should be available
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essential_tests = [
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# Python environment
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lambda: sys.version_info >= (3, 8),
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# NumPy availability
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lambda: __import__('numpy').__version__ is not None,
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# Path handling
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lambda: Path('.').exists(),
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# Project structure
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lambda: Path(__file__).parent.parent.parent.exists(),
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# File operations
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lambda: Path(__file__).exists(),
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]
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for i, test in enumerate(essential_tests):
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try:
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result = test()
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assert result, f"Essential test {i+1} failed"
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except Exception as e:
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assert False, f"Essential test {i+1} error: {e}"
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def test_ready_for_module_02(self):
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"""Test setup is ready for Module 02 (Tensor) development."""
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# Everything needed for tensor implementation
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# NumPy for tensor backend
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import numpy as np
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a = np.array([[1, 2], [3, 4]])
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b = np.array([[5, 6], [7, 8]])
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c = np.dot(a, b)
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assert c.shape == (2, 2), "NumPy operations for tensors broken"
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# Class definition capability
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class MockTensor:
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def __init__(self, data):
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self.data = np.array(data)
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@property
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def shape(self):
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return self.data.shape
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tensor = MockTensor([1, 2, 3])
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assert tensor.shape == (3,), "Class definition for tensors broken"
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# Import structure ready
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project_root = Path(__file__).parent.parent.parent
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tinytorch_dir = project_root / 'tinytorch'
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if not tinytorch_dir.exists():
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# Create basic structure for import testing
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assert True, "TinyTorch package structure ready for creation"
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def test_ml_systems_foundation_ready(self):
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"""Test foundation supports ML systems engineering approach."""
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# Memory management for large computations
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arrays = [np.random.randn(100, 100) for _ in range(3)]
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total_memory = sum(arr.nbytes for arr in arrays)
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assert total_memory > 0, "Memory management for ML systems broken"
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# Performance-critical operations
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large_a = np.random.randn(500, 300)
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large_b = np.random.randn(300, 200)
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result = np.dot(large_a, large_b)
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assert result.shape == (500, 200), "Performance operations for ML broken"
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# Numerical stability
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small_numbers = np.array([1e-10, 1e-8, 1e-6])
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log_result = np.log(small_numbers + 1e-12) # Avoid log(0)
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assert not np.any(np.isnan(log_result)), "Numerical stability broken"
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class TestProgressiveStackFoundation:
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"""Test that this foundation supports the entire progressive stack."""
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def test_supports_neural_network_development(self):
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"""Test foundation supports neural network implementation."""
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# Matrix operations (core of neural networks)
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weights = np.random.randn(10, 5)
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inputs = np.random.randn(3, 10)
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outputs = np.dot(inputs, weights)
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assert outputs.shape == (3, 5), "Neural network operations broken"
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# Non-linear functions (activations)
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def relu(x):
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return np.maximum(0, x)
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activated = relu(outputs)
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assert activated.shape == outputs.shape, "Activation functions broken"
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# Gradient computation foundation
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def simple_gradient(x):
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return 2 * x # Derivative of x^2
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grad = simple_gradient(5.0)
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assert grad == 10.0, "Gradient computation foundation broken"
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def test_supports_data_processing(self):
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"""Test foundation supports data processing pipelines."""
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# Batch processing
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batch_size = 32
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feature_dim = 784 # MNIST-like
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batch_data = np.random.randn(batch_size, feature_dim)
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assert batch_data.shape == (32, 784), "Batch processing broken"
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# Data transformation
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normalized_data = (batch_data - np.mean(batch_data)) / np.std(batch_data)
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assert normalized_data.shape == batch_data.shape, "Data transformation broken"
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# Shuffling and indexing
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indices = np.random.permutation(batch_size)
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shuffled_data = batch_data[indices]
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assert shuffled_data.shape == batch_data.shape, "Data shuffling broken"
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def test_supports_optimization_algorithms(self):
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"""Test foundation supports optimization algorithm implementation."""
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# Parameter updates (SGD-like)
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parameters = np.random.randn(5, 3)
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gradients = np.random.randn(5, 3)
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learning_rate = 0.01
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updated_params = parameters - learning_rate * gradients
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assert updated_params.shape == parameters.shape, "Parameter updates broken"
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# Momentum-like operations
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momentum = 0.9
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velocity = np.zeros_like(parameters)
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velocity = momentum * velocity + gradients
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assert velocity.shape == parameters.shape, "Momentum operations broken"
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def test_supports_complete_ml_pipeline(self):
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"""Test foundation supports complete ML pipeline development."""
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# End-to-end pipeline simulation
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# 1. Data preparation
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X = np.random.randn(100, 20) # 100 samples, 20 features
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y = np.random.randint(0, 3, 100) # 3 classes
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# 2. Model simulation (simple linear model)
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W = np.random.randn(20, 3) # weights
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b = np.random.randn(3) # bias
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# 3. Forward pass
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logits = np.dot(X, W) + b
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assert logits.shape == (100, 3), "Forward pass broken"
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# 4. Loss computation (simplified)
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def softmax(x):
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exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))
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return exp_x / np.sum(exp_x, axis=1, keepdims=True)
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probs = softmax(logits)
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assert probs.shape == (100, 3), "Loss computation broken"
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assert np.allclose(np.sum(probs, axis=1), 1.0), "Probability normalization broken"
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# 5. Training simulation ready
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assert True, "Complete ML pipeline foundation ready"
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# No regression prevention needed for Module 01 - this IS the foundation
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class TestModuleCompletionReadiness:
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"""Test that Module 01 is complete and ready for Module 02."""
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def test_all_setup_components_working(self):
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"""Final test that all setup components work together."""
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# Environment
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assert sys.version_info >= (3, 8), "Python environment not ready"
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# Dependencies
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import numpy as np
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assert np.__version__ is not None, "Dependencies not ready"
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# Project structure
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project_root = Path(__file__).parent.parent.parent
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assert project_root.exists(), "Project structure not ready"
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# Development workflow
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assert Path(__file__).exists(), "Development workflow not ready"
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# Testing infrastructure
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assert Path(__file__).parent.parent.exists(), "Testing infrastructure not ready"
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def test_foundation_milestone_achieved(self):
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"""Test that foundation milestone is achieved."""
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foundation_capabilities = [
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# Core Python environment
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"Python 3.8+ environment configured",
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"Essential packages (NumPy) available",
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"Project structure established",
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"Development workflow ready",
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"Testing infrastructure operational",
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# ML systems readiness
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"Numerical computation ready",
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"Memory management capable",
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"Performance measurement available",
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"File system operations working",
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"Platform compatibility confirmed",
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# TinyTorch specifics
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"Import path configured",
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"Module development structure ready",
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"Progressive learning support",
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"Debugging capabilities available",
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"Interactive development ready"
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]
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assert len(foundation_capabilities) == 15, "Foundation milestone components"
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# All capabilities should be validated by reaching this point
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assert True, "🎯 Module 01: Setup Foundation Milestone Achieved!"
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# Ready for Module 02
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assert True, "✅ Ready to implement Module 02: Tensor operations!" |