def testMatmulExecution(self):
data_a = np.random.randn(10, 20)
data_b = np.random.randn(20)
a = tensor(data_a, chunk_size=2)
b = tensor(data_b, chunk_size=3)
c = matmul(a, b)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.matmul(data_a, data_b)
np.testing.assert_allclose(res, expected)
data_a = np.random.randn(10, 20)
data_b = np.random.randn(10)
a = tensor(data_a, chunk_size=2)
b = tensor(data_b, chunk_size=3)
c = matmul(b, a)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.matmul(data_b, data_a)
np.testing.assert_allclose(res, expected)
data_a = np.random.randn(15, 1, 20, 30)
data_b = np.random.randn(1, 11, 30, 20)
a = tensor(data_a, chunk_size=12)
b = tensor(data_b, chunk_size=13)
c = matmul(a, b)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.matmul(data_a, data_b)
np.testing.assert_allclose(res, expected, atol=.0001)
a = arange(2 * 2 * 4, chunk_size=1).reshape((2, 2, 4))
b = arange(2 * 2 * 4, chunk_size=1).reshape((2, 4, 2))
c = matmul(a, b)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.matmul(np.arange(2 * 2 * 4).reshape(2, 2, 4),
np.arange(2 * 2 * 4).reshape(2, 4, 2))
np.testing.assert_allclose(res, expected, atol=.0001)
data_a = sps.random(10, 20)
data_b = sps.random(20, 5)
a = tensor(data_a, chunk_size=2)
b = tensor(data_b, chunk_size=3)
c = matmul(a, b)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.matmul(data_a.toarray(), data_b.toarray())
np.testing.assert_allclose(res.toarray(), expected)
def testMatmul(self):
t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse()
t2 = t1.T
t3 = matmul(t1, t2, out=empty((2, 2), dtype=t1.dtype, order='F'))
self.assertEqual(t3.order.value, 'F')
with self.assertRaises(TypeError):
matmul(t1, t2, out=1)
with self.assertRaises(TypeError):
matmul(t1, t2, out=empty((2, 2), dtype='?'))
with self.assertRaises(ValueError):
matmul(t1, t2, out=empty((3, 2), dtype=t1.dtype))
raw1 = np.asfortranarray(np.random.rand(3, 3))
raw2 = np.asfortranarray(np.random.rand(3, 3))
raw3 = np.random.rand(3, 3)
self.assertEqual(matmul(tensor(raw1), tensor(raw2)).flags['C_CONTIGUOUS'],
np.matmul(raw1, raw2).flags['C_CONTIGUOUS'])
self.assertEqual(matmul(tensor(raw1), tensor(raw2)).flags['F_CONTIGUOUS'],
np.matmul(raw1, raw2).flags['F_CONTIGUOUS'])
self.assertEqual(matmul(tensor(raw1), tensor(raw2), order='A').flags['C_CONTIGUOUS'],
np.matmul(raw1, raw2, order='A').flags['C_CONTIGUOUS'])
self.assertEqual(matmul(tensor(raw1), tensor(raw2), order='A').flags['F_CONTIGUOUS'],
np.matmul(raw1, raw2, order='A').flags['F_CONTIGUOUS'])
self.assertEqual(matmul(tensor(raw1), tensor(raw3), order='A').flags['C_CONTIGUOUS'],
np.matmul(raw1, raw3, order='A').flags['C_CONTIGUOUS'])
self.assertEqual(matmul(tensor(raw1), tensor(raw3), order='A').flags['F_CONTIGUOUS'],
np.matmul(raw1, raw3, order='A').flags['F_CONTIGUOUS'])
def testMatmul(self):
a_data = [[1, 0], [0, 1]]
b_data = [[4, 1], [2, 2]]
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
self.assertEqual(t.shape, (2, 2))
t.tiles()
self.assertEqual(t.shape, tuple(sum(s) for s in t.nsplits))
b_data = [1, 2]
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
self.assertEqual(t.shape, (2, ))
t.tiles()
self.assertEqual(t.shape, tuple(sum(s) for s in t.nsplits))
t = matmul(b, a)
self.assertEqual(t.shape, (2, ))
t.tiles()
self.assertEqual(t.shape, tuple(sum(s) for s in t.nsplits))
a_data = np.arange(2 * 2 * 4).reshape((2, 2, 4))
b_data = np.arange(2 * 2 * 4).reshape((2, 4, 2))
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
self.assertEqual(t.shape, (2, 2, 2))
t.tiles()
self.assertEqual(t.shape, tuple(sum(s) for s in t.nsplits))
t = matmul(tensor([2j, 3j], chunk_size=1),
tensor([2j, 3j], chunk_size=1))
self.assertEqual(t.shape, ())
t.tiles()
self.assertEqual(t.shape, tuple(sum(s) for s in t.nsplits))
with self.assertRaises(ValueError):
matmul([1, 2], 3)
with self.assertRaises(ValueError):
matmul(np.random.randn(2, 3, 4), np.random.randn(3, 4, 3))
t = matmul(tensor(np.random.randn(2, 3, 4), chunk_size=2),
tensor(np.random.randn(3, 1, 4, 3), chunk_size=3))
self.assertEqual(t.shape, (3, 2, 3, 3))
v = ones((100, 100), chunk_size=10)
tv = matmul(v, v)
self.assertEqual(tv.shape, (100, 100))
tv.tiles()
self.assertEqual(tv.shape, tuple(sum(s) for s in tv.nsplits))
def test_matmul():
t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse()
t2 = t1.T
t3 = matmul(t1, t2, out=empty((2, 2), dtype=t1.dtype, order='F'))
assert t3.order.value == 'F'
with pytest.raises(TypeError):
matmul(t1, t2, out=1)
with pytest.raises(TypeError):
matmul(t1, t2, out=empty((2, 2), dtype='?'))
with pytest.raises(ValueError):
matmul(t1, t2, out=empty((3, 2), dtype=t1.dtype))
raw1 = np.asfortranarray(np.random.rand(3, 3))
raw2 = np.asfortranarray(np.random.rand(3, 3))
raw3 = np.random.rand(3, 3)
assert matmul(tensor(raw1), tensor(raw2)).flags['C_CONTIGUOUS'] == \
np.matmul(raw1, raw2).flags['C_CONTIGUOUS']
assert matmul(tensor(raw1), tensor(raw2)).flags['F_CONTIGUOUS'] == \
np.matmul(raw1, raw2).flags['F_CONTIGUOUS']
assert matmul(tensor(raw1), tensor(raw2), order='A').flags['C_CONTIGUOUS'] == \
np.matmul(raw1, raw2, order='A').flags['C_CONTIGUOUS']
assert matmul(tensor(raw1), tensor(raw2), order='A').flags['F_CONTIGUOUS'] == \
np.matmul(raw1, raw2, order='A').flags['F_CONTIGUOUS']
assert matmul(tensor(raw1), tensor(raw3), order='A').flags['C_CONTIGUOUS'] == \
np.matmul(raw1, raw3, order='A').flags['C_CONTIGUOUS']
assert matmul(tensor(raw1), tensor(raw3), order='A').flags['F_CONTIGUOUS'] == \
np.matmul(raw1, raw3, order='A').flags['F_CONTIGUOUS']
def test_matmul_execution(setup):
rs = np.random.RandomState(0)
data_a = rs.randn(10, 20)
data_b = rs.randn(20)
a = tensor(data_a, chunk_size=5)
b = tensor(data_b, chunk_size=6)
c = matmul(a, b)
res = c.execute().fetch()
expected = np.matmul(data_a, data_b)
np.testing.assert_allclose(res, expected)
data_a = rs.randn(10, 20)
data_b = rs.randn(10)
a = tensor(data_a, chunk_size=5)
b = tensor(data_b, chunk_size=6)
c = matmul(b, a)
res = c.execute().fetch()
expected = np.matmul(data_b, data_a)
np.testing.assert_allclose(res, expected)
data_a = rs.randn(15, 1, 20, 30)
data_b = rs.randn(1, 11, 30, 20)
a = tensor(data_a, chunk_size=12)
b = tensor(data_b, chunk_size=13)
c = matmul(a, b)
res = c.execute().fetch()
expected = np.matmul(data_a, data_b)
np.testing.assert_allclose(res, expected, atol=.0001)
a = arange(2 * 2 * 4, chunk_size=1).reshape((2, 2, 4))
b = arange(2 * 2 * 4, chunk_size=1).reshape((2, 4, 2))
c = matmul(a, b)
res = c.execute().fetch()
expected = np.matmul(
np.arange(2 * 2 * 4).reshape(2, 2, 4),
np.arange(2 * 2 * 4).reshape(2, 4, 2))
np.testing.assert_allclose(res, expected, atol=.0001)
data_a = sps.random(10, 20)
data_b = sps.random(20, 5)
a = tensor(data_a, chunk_size=5)
b = tensor(data_b, chunk_size=6)
c = matmul(a, b)
res = c.execute().fetch()
expected = np.matmul(data_a.toarray(), data_b.toarray())
np.testing.assert_allclose(res.toarray(), expected)
# test order
data_a = np.asfortranarray(rs.randn(10, 20))
data_b = np.asfortranarray(rs.randn(20, 30))
a = tensor(data_a, chunk_size=12)
b = tensor(data_b, chunk_size=13)
c = matmul(a, b)
res = c.execute().fetch()
expected = np.matmul(data_a, data_b)
np.testing.assert_allclose(res, expected)
assert res.flags['C_CONTIGUOUS'] == expected.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == expected.flags['F_CONTIGUOUS']
c = matmul(a, b, order='A')
res = c.execute().fetch()
expected = np.matmul(data_a, data_b, order='A')
np.testing.assert_allclose(res, expected)
assert res.flags['C_CONTIGUOUS'] == expected.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == expected.flags['F_CONTIGUOUS']
c = matmul(a, b, order='C')
res = c.execute().fetch()
expected = np.matmul(data_a, data_b, order='C')
np.testing.assert_allclose(res, expected)
assert res.flags['C_CONTIGUOUS'] == expected.flags['C_CONTIGUOUS']
assert res.flags['F_CONTIGUOUS'] == expected.flags['F_CONTIGUOUS']
def test_matmul():
a_data = [[1, 0], [0, 1]]
b_data = [[4, 1], [2, 2]]
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
assert t.shape == (2, 2)
t = tile(t)
assert t.shape == tuple(sum(s) for s in t.nsplits)
b_data = [1, 2]
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
assert t.shape == (2, )
t = tile(t)
assert t.shape == tuple(sum(s) for s in t.nsplits)
t = matmul(b, a)
assert t.shape == (2, )
t = tile(t)
assert t.shape == tuple(sum(s) for s in t.nsplits)
a_data = np.arange(2 * 2 * 4).reshape((2, 2, 4))
b_data = np.arange(2 * 2 * 4).reshape((2, 4, 2))
a = tensor(a_data, chunk_size=1)
b = tensor(b_data, chunk_size=1)
t = matmul(a, b)
assert t.shape == (2, 2, 2)
t = tile(t)
assert t.shape == tuple(sum(s) for s in t.nsplits)
t = matmul(tensor([2j, 3j], chunk_size=1), tensor([2j, 3j], chunk_size=1))
assert t.shape == ()
t = tile(t)
assert t.shape == tuple(sum(s) for s in t.nsplits)
with pytest.raises(ValueError):
matmul([1, 2], 3)
with pytest.raises(ValueError):
matmul(np.random.randn(2, 3, 4), np.random.randn(3, 4, 3))
t = matmul(tensor(np.random.randn(2, 3, 4), chunk_size=2),
tensor(np.random.randn(3, 1, 4, 3), chunk_size=3))
assert t.shape == (3, 2, 3, 3)
v = ones((100, 100), chunk_size=10)
tv = matmul(v, v)
assert tv.shape == (100, 100)
tv = tile(tv)
assert tv.shape == tuple(sum(s) for s in tv.nsplits)
