def test_layout():
"Test basis properties of layout and strides"
data = [0] * 3 * 5
tensor_data = minitorch.TensorData(data, (3, 5), (5, 1))
assert tensor_data.is_contiguous()
assert tensor_data.shape == (3, 5)
assert tensor_data.index((1, 0)) == 5
assert tensor_data.index((1, 2)) == 7
tensor_data = minitorch.TensorData(data, (5, 3), (1, 5))
assert tensor_data.shape == (5, 3)
assert not tensor_data.is_contiguous()
data = [0] * 4 * 2 * 2
tensor_data = minitorch.TensorData(data, (4, 2, 2))
assert tensor_data.strides == (4, 2, 1)
def tensor_data(draw, numbers=floats(), shape=None):
if shape is None:
shape = draw(shapes())
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
permute = draw(permutations(range(len(shape))))
permute_shape = tuple([shape[i] for i in permute])
reverse_permute = [a[0] for a in sorted(enumerate(permute), key=lambda a: a[1])]
td = minitorch.TensorData(data, permute_shape)
ret = td.permute(*reverse_permute)
assert ret.shape[0] == shape[0]
return ret
def matmul_tensors(
draw, numbers=floats(allow_nan=False, min_value=-100, max_value=100)
):
i, j, k = [draw(integers(min_value=1, max_value=10)) for _ in range(3)]
l1 = (i, j)
l2 = (j, k)
values = []
for shape in [l1, l2]:
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
values.append(minitorch.Tensor(minitorch.TensorData(data, shape)))
return values
def shaped_tensors(
draw,
n,
numbers=floats(allow_nan=False, min_value=-100, max_value=100),
backend=minitorch.TensorFunctions,
):
td = draw(tensor_data(numbers))
values = []
for i in range(n):
data = draw(lists(numbers, min_size=td.size, max_size=td.size))
values.append(
minitorch.Tensor(minitorch.TensorData(data, td.shape),
backend=backend))
return values
def tensor_data(draw, numbers=floats(), shape=None):
if shape is None:
shape = draw(shapes())
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
return minitorch.TensorData(data, shape)
def test_layout_bad():
"Test basis properties of layout and strides"
data = [0] * 3 * 5
minitorch.TensorData(data, (3, 5), (6, ))
