def test_equality():
assert Tensor.from_lol([0, 2, 0],
format='d') == Tensor.from_lol([0, 2, 0],
format='s')
assert Tensor.from_lol([0, 1, 0], format='d') != Tensor.from_lol(
[0, 2, 0], format='s')
assert Tensor.from_lol([0, 1, 0], format='d') != 1
def test_matrix_multiply(a, b, c):
a = Tensor.from_lol(a)
b = Tensor.from_lol(b)
if isinstance(c, list):
c = Tensor.from_lol(c)
actual = a @ b
assert actual == c
def test_subtract(a, b, c):
if isinstance(a, list):
a = Tensor.from_lol(a)
if isinstance(b, list):
b = Tensor.from_lol(b)
expected = Tensor.from_lol(c)
actual = a - b
assert actual == expected
def test_multiply(a, b, c):
if isinstance(a, list):
a = Tensor.from_lol(a)
if isinstance(b, list):
b = Tensor.from_lol(b)
expected = Tensor.from_lol(c)
actual = a * b
assert actual == expected
def test_binary_mismatched_dimensions():
a = Tensor.from_lol([3, 2, 5])
b = Tensor.from_lol([1, 2, 0, 4])
with pytest.raises(ValueError):
_ = a + b
with pytest.raises(ValueError):
_ = a - b
with pytest.raises(ValueError):
_ = a * b
def assert_same_as_dense(expression, format_out, **tensor_pairs):
tensors_in_format = {
name: Tensor.from_lol(data, format=format)
for name, (data, format) in tensor_pairs.items()
}
tensors_as_dense = {
name: Tensor.from_lol(data)
for name, (data, _) in tensor_pairs.items()
}
actual = evaluate(expression, format_out, **tensors_in_format)
expected = evaluate(expression,
''.join('d' for c in format_out if c in ('d', 's')),
**tensors_as_dense)
assert actual == expected
def test_from_dense_lil_scalar():
format = Format((), ())
x = Tensor.from_lol(2.0, dimensions=(), format=format)
assert x.order == 0
assert x.dimensions == ()
assert x.modes == ()
assert x.mode_ordering == ()
assert x.format == format
assert x.to_dok() == {(): 2.0}
def test_matrix_multiply_too_many_dimensions():
a = Tensor.from_lol([3, 2, 5])
b = Tensor.from_dok(
{
(0, 0, 0): 4.5,
(1, 0, 1): 3.2,
(1, 1, 2): -3.0,
(0, 1, 1): 5.0,
},
dimensions=(3, 3, 3))
with pytest.raises(ValueError):
_ = a @ b
def test_from_dense_lil():
format = Format((Mode.dense, Mode.dense), (0, 1))
x = Tensor.from_lol(
[[0, -4.0, 4.5], [0, -3.5, 2.5]],
dimensions=(2, 3),
format=format,
)
assert x.order == 2
assert x.dimensions == (2, 3)
assert x.modes == (Mode.dense, Mode.dense)
assert x.mode_ordering == (0, 1)
assert x.format == format
assert x.to_dok() == {
(0, 1): -4.0,
(0, 2): 4.5,
(1, 1): -3.5,
(1, 2): 2.5,
}
def test_copy_2(dense, format_in, format_out):
a = Tensor.from_lol(dense, format=format_in)
actual = evaluate('b(i,j) = a(i,j)', format_out, a=a)
assert actual == a
def test_figure_2a(format, indices, vals):
data = [5, 1, 0, 0, 2, 0, 8, 0]
a = Tensor.from_lol(data, dimensions=(8, ), format=format)
assert a.taco_indices == indices
assert a.taco_vals == vals
def test_str_repr():
# Just make sure these run
a = Tensor.from_lol([0, 2, 1, 0])
str(a)
repr(a)
def test_matrix_multiply_mismatched_dimensions():
a = Tensor.from_lol([3, 2, 5])
b = Tensor.from_lol([1, 2, 0, 4])
with pytest.raises(ValueError):
_ = a @ b
def test_nonscalar_to_float():
x = Tensor.from_lol([1, 2])
with pytest.raises(ValueError):
_ = float(x)
def test_figure_5(format, indices, vals):
data = [[6, 0, 9, 8], [0, 0, 0, 0], [5, 0, 0, 7]]
A = Tensor.from_lol(data, dimensions=(3, 4), format=format)
assert A.taco_indices == indices
assert A.taco_vals == vals
return tensor;
}
"""
ffi = FFI()
ffi.include(tensor_cdefs)
ffi.cdef("""
taco_tensor_t create_tensor();
taco_tensor_t* create_pointer_to_tensor();
""")
ffi.set_source(
'taco_kernel',
taco_define_header + taco_type_header + source,
extra_compile_args=['-Wno-unused-variable', '-Wno-unknown-pragmas'])
expected_tensor = Tensor.from_lol([[6, 0, 9, 8], [0, 0, 0, 0], [5, 0, 0, 7]],
format='ds')
with tempfile.TemporaryDirectory() as temp_dir:
# Lock because FFI.compile is not thread safe: https://foss.heptapod.net/pypy/cffi/-/issues/490
with lock:
# Create shared object in temporary directory
lib_path = ffi.compile(tmpdir=temp_dir)
# Load the shared object
lib = ffi.dlopen(lib_path)
def test_take_ownership_of_tensor_on_returned_struct():
cffi_tensor = lib.create_tensor()
take_ownership_of_tensor_members(cffi_tensor)
tensor = Tensor(cffi_tensor)