def test_multithread_evaluation():
# As of version 1.14.4 of cffi, the FFI.compile method is not thread safe. This tests that evaluation of different
# kernels is thread safe.
A = Tensor.from_aos([[1, 0], [0, 1], [1, 2]], [2.0, -2.0, 4.0],
dimensions=(2, 3),
format='ds')
x = Tensor.from_aos([[0], [1], [2]], [3.0, 2.5, 2.0],
dimensions=(3, ),
format='d')
def run_eval():
# Generate a random expression so that the cache cannot be hit
return evaluate(f'y{randrange(1024)}(i) = A(i,j) * x(j)',
'd',
A=A,
x=x)
n = 4
with ThreadPool(n) as p:
results = p.starmap(run_eval, [()] * n)
expected = run_eval()
for actual in results:
assert actual == expected
def test_unsorted_coordinates(format, indices, vals, permutation):
data = [
((0, 0), 6),
((0, 2), 9),
((0, 3), 8),
((2, 0), 5),
((2, 3), 7),
]
permutated_data = [data[i] for i in permutation]
coordinates, values = zip(*permutated_data)
A = Tensor.from_aos(coordinates, values, dimensions=(3, 4), format=format)
assert A.taco_indices == indices
assert A.taco_vals == vals
A = Tensor.from_soa(zip(*coordinates),
values,
dimensions=(3, 4),
format=format)
assert A.taco_indices == indices
assert A.taco_vals == vals
A = Tensor.from_dok(dict(permutated_data),
dimensions=(3, 4),
format=format)
assert A.taco_indices == indices
assert A.taco_vals == vals
def test_csr_matrix_vector_product():
A = Tensor.from_aos([[1, 0], [0, 1], [1, 2]], [2.0, -2.0, 4.0],
dimensions=(2, 3),
format='ds')
x = Tensor.from_aos([[0], [1], [2]], [3.0, 2.5, 2.0],
dimensions=(3, ),
format='d')
expected = Tensor.from_aos([[0], [1]], [-5.0, 14.0],
dimensions=(2, ),
format='d')
function = tensor_method('y(i) = A(i,j) * x(j)', dict(A='ds', x='d'), 'd')
actual = function(A, x)
assert actual == expected
actual = evaluate('y(i) = A(i,j) * x(j)', 'd', A=A, x=x)
assert actual == expected
def test_csr_matrix_plus_csr_matrix():
A = Tensor.from_aos([[1, 0], [0, 1], [1, 2]], [2.0, -2.0, 4.0],
dimensions=(2, 3),
format='ds')
B = Tensor.from_aos([[1, 1], [1, 2], [0, 2]], [-3.0, 4.0, 3.5],
dimensions=(2, 3),
format='ds')
expected = Tensor.from_aos([[1, 0], [0, 1], [1, 2], [1, 1], [0, 2]],
[2.0, -2.0, 8.0, -3.0, 3.5],
dimensions=(2, 3),
format='ds')
function = tensor_method('C(i,j) = A(i,j) + B(i,j)', dict(A='ds', B='ds'),
'ds')
actual = function(A, B)
assert actual == expected
actual = evaluate('C(i,j) = A(i,j) * B(i,j)', 'ds', A=A, B=B)
assert actual == expected
def test_from_aos():
format = Format((Mode.dense, Mode.compressed), (1, 0))
x = Tensor.from_aos(
[(1, 0), (1, 1), (2, 1), (3, 1)],
[4.5, 3.2, -3.0, 5.0],
dimensions=(4, 3),
format=format,
)
assert x.order == 2
assert x.dimensions == (4, 3)
assert x.modes == (Mode.dense, Mode.compressed)
assert x.mode_ordering == (1, 0)
assert x.format == format
assert x.to_dok() == {
(1, 0): 4.5,
(1, 1): 3.2,
(2, 1): -3.0,
(3, 1): 5.0,
}
def a_ds():
return Tensor.from_aos([[1, 0], [0, 1], [1, 2]], [2.0, -2.0, 4.0],
dimensions=(2, 3),
format='ds')
def c_ds_add():
return Tensor.from_aos([[1, 0], [0, 1], [1, 2], [1, 1], [0, 2]],
[2.0, -2.0, 8.0, -3.0, 3.5],
dimensions=(2, 3),
format='ds')
def b_ds():
return Tensor.from_aos([[1, 1], [1, 2], [0, 2]], [-3.0, 4.0, 3.5],
dimensions=(2, 3),
format='ds')