mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-06-03 19:03:06 -05:00
e86d0223d1
- Fix CLI tool to look for tests in modules/{module}/tests/ instead of tests/
- Update test imports to use parent directory module imports
- Update Cursor rules to reflect new test structure:
* Project structure shows tests/ subdirectory
* Testing patterns show correct paths and import patterns
* Development workflow shows updated test locations
- Test imports now work: from tensor_dev import Tensor
- CLI commands now find tests in correct locations
Tests are now properly organized and discoverable
311 lines
9.7 KiB
Python
311 lines
9.7 KiB
Python
"""
|
|
Tests for TinyTorch Tensor module.
|
|
|
|
Tests the core tensor functionality including creation, arithmetic operations,
|
|
utility methods, and edge cases.
|
|
"""
|
|
|
|
import sys
|
|
import os
|
|
import pytest
|
|
import numpy as np
|
|
|
|
# Add the parent directory to path to import tensor_dev
|
|
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
|
|
|
|
# Import from the module's development file
|
|
from tensor_dev import Tensor
|
|
|
|
class TestTensorCreation:
|
|
"""Test tensor creation from different data types."""
|
|
|
|
def test_scalar_creation(self):
|
|
"""Test creating tensors from scalars."""
|
|
# Float scalar
|
|
t1 = Tensor(5.0)
|
|
assert t1.shape == ()
|
|
assert t1.size == 1
|
|
assert t1.item() == 5.0
|
|
|
|
# Integer scalar
|
|
t2 = Tensor(42)
|
|
assert t2.shape == ()
|
|
assert t2.size == 1
|
|
assert t2.item() == 42.0 # Should convert to float32
|
|
|
|
def test_vector_creation(self):
|
|
"""Test creating 1D tensors."""
|
|
t = Tensor([1, 2, 3, 4])
|
|
assert t.shape == (4,)
|
|
assert t.size == 4
|
|
assert t.dtype == np.int32 # Integer list defaults to int32
|
|
np.testing.assert_array_equal(t.numpy(), [1, 2, 3, 4])
|
|
|
|
def test_matrix_creation(self):
|
|
"""Test creating 2D tensors."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
assert t.shape == (2, 2)
|
|
assert t.size == 4
|
|
expected = np.array([[1.0, 2.0], [3.0, 4.0]], dtype='float32')
|
|
np.testing.assert_array_equal(t.numpy(), expected)
|
|
|
|
def test_numpy_array_creation(self):
|
|
"""Test creating tensors from numpy arrays."""
|
|
arr = np.array([1, 2, 3], dtype='int32')
|
|
t = Tensor(arr)
|
|
assert t.shape == (3,)
|
|
assert t.dtype in ['int32', 'float32'] # May convert
|
|
|
|
def test_dtype_specification(self):
|
|
"""Test explicit dtype specification."""
|
|
t = Tensor([1, 2, 3], dtype='int32')
|
|
assert t.dtype == np.int32
|
|
|
|
def test_invalid_data_type(self):
|
|
"""Test error handling for invalid data types."""
|
|
with pytest.raises(TypeError):
|
|
Tensor("invalid")
|
|
with pytest.raises(TypeError):
|
|
Tensor({"dict": "invalid"})
|
|
|
|
class TestTensorProperties:
|
|
"""Test tensor properties and methods."""
|
|
|
|
def test_shape_property(self):
|
|
"""Test shape property for different dimensions."""
|
|
assert Tensor(5).shape == ()
|
|
assert Tensor([1, 2, 3]).shape == (3,)
|
|
assert Tensor([[1, 2], [3, 4]]).shape == (2, 2)
|
|
assert Tensor([[[1]]]).shape == (1, 1, 1)
|
|
|
|
def test_size_property(self):
|
|
"""Test size property."""
|
|
assert Tensor(5).size == 1
|
|
assert Tensor([1, 2, 3]).size == 3
|
|
assert Tensor([[1, 2], [3, 4]]).size == 4
|
|
assert Tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).size == 8
|
|
|
|
def test_dtype_property(self):
|
|
"""Test dtype property."""
|
|
t1 = Tensor(5.0)
|
|
assert t1.dtype == np.float32
|
|
|
|
t2 = Tensor([1, 2, 3], dtype='int32')
|
|
assert t2.dtype == np.int32
|
|
|
|
def test_repr(self):
|
|
"""Test string representation."""
|
|
t = Tensor([1, 2, 3])
|
|
repr_str = repr(t)
|
|
assert 'Tensor' in repr_str
|
|
assert 'shape=' in repr_str
|
|
assert 'dtype=' in repr_str
|
|
|
|
class TestArithmeticOperations:
|
|
"""Test tensor arithmetic operations."""
|
|
|
|
def test_tensor_addition(self):
|
|
"""Test tensor + tensor addition."""
|
|
a = Tensor([1, 2, 3])
|
|
b = Tensor([4, 5, 6])
|
|
result = a + b
|
|
expected = [5.0, 7.0, 9.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_scalar_addition(self):
|
|
"""Test tensor + scalar addition."""
|
|
a = Tensor([1, 2, 3])
|
|
result = a + 10
|
|
expected = [11.0, 12.0, 13.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_reverse_addition(self):
|
|
"""Test scalar + tensor addition."""
|
|
a = Tensor([1, 2, 3])
|
|
result = 10 + a
|
|
expected = [11.0, 12.0, 13.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_tensor_subtraction(self):
|
|
"""Test tensor - tensor subtraction."""
|
|
a = Tensor([5, 7, 9])
|
|
b = Tensor([1, 2, 3])
|
|
result = a - b
|
|
expected = [4.0, 5.0, 6.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_scalar_subtraction(self):
|
|
"""Test tensor - scalar subtraction."""
|
|
a = Tensor([10, 20, 30])
|
|
result = a - 5
|
|
expected = [5.0, 15.0, 25.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_tensor_multiplication(self):
|
|
"""Test tensor * tensor multiplication."""
|
|
a = Tensor([2, 3, 4])
|
|
b = Tensor([5, 6, 7])
|
|
result = a * b
|
|
expected = [10.0, 18.0, 28.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_scalar_multiplication(self):
|
|
"""Test tensor * scalar multiplication."""
|
|
a = Tensor([1, 2, 3])
|
|
result = a * 3
|
|
expected = [3.0, 6.0, 9.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_reverse_multiplication(self):
|
|
"""Test scalar * tensor multiplication."""
|
|
a = Tensor([1, 2, 3])
|
|
result = 3 * a
|
|
expected = [3.0, 6.0, 9.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_tensor_division(self):
|
|
"""Test tensor / tensor division."""
|
|
a = Tensor([6, 8, 10])
|
|
b = Tensor([2, 4, 5])
|
|
result = a / b
|
|
expected = [3.0, 2.0, 2.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_scalar_division(self):
|
|
"""Test tensor / scalar division."""
|
|
a = Tensor([6, 8, 10])
|
|
result = a / 2
|
|
expected = [3.0, 4.0, 5.0]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
class TestUtilityMethods:
|
|
"""Test tensor utility methods."""
|
|
|
|
def test_reshape(self):
|
|
"""Test tensor reshaping."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
reshaped = t.reshape(4)
|
|
assert reshaped.shape == (4,)
|
|
expected = [1.0, 2.0, 3.0, 4.0]
|
|
np.testing.assert_array_equal(reshaped.numpy(), expected)
|
|
|
|
# Reshape to 2D
|
|
reshaped2 = t.reshape(1, 4)
|
|
assert reshaped2.shape == (1, 4)
|
|
|
|
def test_transpose(self):
|
|
"""Test tensor transpose."""
|
|
t = Tensor([[1, 2, 3], [4, 5, 6]])
|
|
transposed = t.transpose()
|
|
assert transposed.shape == (3, 2)
|
|
expected = [[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]]
|
|
np.testing.assert_array_equal(transposed.numpy(), expected)
|
|
|
|
def test_sum_all(self):
|
|
"""Test summing all elements."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
result = t.sum()
|
|
assert result.item() == 10.0
|
|
|
|
def test_sum_axis(self):
|
|
"""Test summing along specific axes."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
|
|
# Sum along axis 0 (columns)
|
|
sum0 = t.sum(axis=0)
|
|
expected0 = [4.0, 6.0]
|
|
np.testing.assert_array_equal(sum0.numpy(), expected0)
|
|
|
|
# Sum along axis 1 (rows)
|
|
sum1 = t.sum(axis=1)
|
|
expected1 = [3.0, 7.0]
|
|
np.testing.assert_array_equal(sum1.numpy(), expected1)
|
|
|
|
def test_mean(self):
|
|
"""Test mean calculation."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
result = t.mean()
|
|
assert result.item() == 2.5
|
|
|
|
def test_max(self):
|
|
"""Test maximum value."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
result = t.max()
|
|
assert result.item() == 4.0
|
|
|
|
def test_min(self):
|
|
"""Test minimum value."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
result = t.min()
|
|
assert result.item() == 1.0
|
|
|
|
def test_item_scalar(self):
|
|
"""Test converting single-element tensor to scalar."""
|
|
t = Tensor(42.0)
|
|
assert t.item() == 42.0
|
|
|
|
# Single element tensor from computation
|
|
t2 = Tensor([5]).sum()
|
|
assert t2.item() == 5.0
|
|
|
|
def test_item_error(self):
|
|
"""Test item() error for multi-element tensors."""
|
|
t = Tensor([1, 2, 3])
|
|
with pytest.raises(ValueError):
|
|
t.item()
|
|
|
|
def test_numpy_conversion(self):
|
|
"""Test converting tensor to numpy array."""
|
|
t = Tensor([[1, 2], [3, 4]])
|
|
arr = t.numpy()
|
|
assert isinstance(arr, np.ndarray)
|
|
assert arr.shape == (2, 2)
|
|
expected = [[1.0, 2.0], [3.0, 4.0]]
|
|
np.testing.assert_array_equal(arr, expected)
|
|
|
|
class TestEdgeCases:
|
|
"""Test edge cases and error conditions."""
|
|
|
|
def test_empty_list(self):
|
|
"""Test creating tensor from empty list."""
|
|
t = Tensor([])
|
|
assert t.shape == (0,)
|
|
assert t.size == 0
|
|
|
|
def test_mixed_operations(self):
|
|
"""Test combining different operations."""
|
|
a = Tensor([[1, 2], [3, 4]])
|
|
b = Tensor([[2, 2], [2, 2]])
|
|
|
|
# Complex expression
|
|
result = (a + b) * 2 - 1
|
|
expected = [[5.0, 7.0], [9.0, 11.0]]
|
|
np.testing.assert_array_equal(result.numpy(), expected)
|
|
|
|
def test_chained_operations(self):
|
|
"""Test chaining multiple operations."""
|
|
t = Tensor([[1, 2, 3], [4, 5, 6]])
|
|
result = t.sum(axis=1).mean()
|
|
# Row sums: [6, 15], mean: 10.5
|
|
assert result.item() == 10.5
|
|
|
|
def run_tensor_tests():
|
|
"""Run all tensor tests and return results."""
|
|
print("🧪 Running Tensor module tests...")
|
|
|
|
# Run tests using pytest
|
|
test_results = pytest.main([
|
|
__file__,
|
|
"-v",
|
|
"--tb=short"
|
|
])
|
|
|
|
return test_results == 0
|
|
|
|
if __name__ == "__main__":
|
|
success = run_tensor_tests()
|
|
if success:
|
|
print("✅ All tensor tests passed!")
|
|
else:
|
|
print("❌ Some tensor tests failed!")
|
|
sys.exit(1) |