mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-05 10:52:53 -05:00
bd7fcb2177
Package exports: - Fix tinytorch/__init__.py to export all required components for milestones - Add Dense as alias for Linear for compatibility - Add loss functions (MSELoss, CrossEntropyLoss, BinaryCrossEntropyLoss) - Export spatial operations, data loaders, and transformer components Test infrastructure: - Create tests/conftest.py to handle path setup - Create tests/test_utils.py with shared test utilities - Rename test_progressive_integration.py files to include module number - Fix syntax errors in test files (spaces in class names) - Remove stale test file referencing non-existent modules Documentation: - Update README.md with correct milestone file names - Fix milestone requirements to match actual module dependencies Export system: - Run tito export --all to regenerate package from source modules - Ensure all 20 modules are properly exported
115 lines
3.1 KiB
Python
115 lines
3.1 KiB
Python
"""
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TinyTorch Test Utilities
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Shared utilities for integration tests across all modules.
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Provides setup functions and common test helpers.
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"""
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import sys
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import os
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from pathlib import Path
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def setup_integration_test():
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"""
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Set up the environment for integration testing.
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This function ensures:
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1. The TinyTorch package is importable
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2. NumPy random seed is set for reproducibility
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3. Warning filters are set appropriately
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Call this at the top of integration test files before importing TinyTorch.
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"""
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import warnings
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import numpy as np
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# Ensure tinytorch is on the path (from project root)
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project_root = Path(__file__).parent.parent
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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# Set random seed for reproducibility
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np.random.seed(42)
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# Suppress certain warnings during tests
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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warnings.filterwarnings('ignore', category=FutureWarning)
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# Set quiet mode for tinytorch imports during tests
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os.environ['TINYTORCH_QUIET'] = '1'
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def get_project_root() -> Path:
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"""Return the project root directory."""
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return Path(__file__).parent.parent
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def get_test_data_path() -> Path:
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"""Return the path to test data directory."""
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return get_project_root() / "datasets"
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def create_test_tensor(shape, requires_grad=True, seed=None):
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"""
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Create a test tensor with random data.
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Args:
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shape: Tuple specifying tensor shape
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requires_grad: Whether tensor should track gradients
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seed: Optional random seed for reproducibility
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Returns:
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Tensor with random data
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"""
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import numpy as np
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from tinytorch.core.tensor import Tensor
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if seed is not None:
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np.random.seed(seed)
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data = np.random.randn(*shape).astype(np.float32)
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return Tensor(data, requires_grad=requires_grad)
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def assert_tensors_close(t1, t2, rtol=1e-5, atol=1e-8, msg=""):
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"""
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Assert that two tensors are element-wise close.
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Args:
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t1: First tensor
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t2: Second tensor
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rtol: Relative tolerance
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atol: Absolute tolerance
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msg: Optional message for assertion error
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"""
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import numpy as np
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# Extract data from tensors if needed
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data1 = t1.data if hasattr(t1, 'data') else t1
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data2 = t2.data if hasattr(t2, 'data') else t2
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if not np.allclose(data1, data2, rtol=rtol, atol=atol):
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diff = np.abs(data1 - data2)
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max_diff = np.max(diff)
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raise AssertionError(
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f"Tensors not close (max diff: {max_diff:.6e}). {msg}"
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)
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def assert_gradients_exist(tensor, msg=""):
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"""Assert that a tensor has computed gradients."""
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if tensor.grad is None:
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raise AssertionError(f"Tensor has no gradients. {msg}")
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def skip_if_no_tinytorch():
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"""Pytest skip decorator for when tinytorch isn't available."""
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import pytest
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try:
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import tinytorch
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return pytest.mark.skipif(False, reason="TinyTorch available")
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except ImportError:
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return pytest.mark.skip(reason="TinyTorch not installed")
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