def test_svd(self):
a = torch.Tensor(((8.79, 6.11, -9.15, 9.57, -3.49, 9.84),
(9.93, 6.91, -7.93, 1.64, 4.02, 0.15),
(9.83, 5.04, 4.86, 8.83, 9.80, -8.99),
(5.45, -0.27, 4.85, 0.74, 10.00, -6.02),
(3.16, 7.98, 3.01, 5.80, 4.27, -5.31))).t().clone()
u, s, v = torch.svd(a)
uu = torch.Tensor()
ss = torch.Tensor()
vv = torch.Tensor()
uuu, sss, vvv = torch.svd(a, out=(uu, ss, vv))
self.assertEqual(u, uu, 0, 'torch.svd')
self.assertEqual(u, uuu, 0, 'torch.svd')
self.assertEqual(s, ss, 0, 'torch.svd')
self.assertEqual(s, sss, 0, 'torch.svd')
self.assertEqual(v, vv, 0, 'torch.svd')
self.assertEqual(v, vvv, 0, 'torch.svd')
# test reuse
X = torch.randn(4, 4)
U, S, V = torch.svd(X)
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
self.assertFalse(U.is_contiguous(), 'U is contiguous')
torch.svd(X, out=(U, S, V))
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
# test non-contiguous
X = torch.randn(5, 5)
U = torch.zeros(5, 2, 5)[:, 1]
S = torch.zeros(5, 2)[:, 1]
V = torch.zeros(5, 2, 5)[:, 1]
self.assertFalse(U.is_contiguous(), 'U is contiguous')
self.assertFalse(S.is_contiguous(), 'S is contiguous')
self.assertFalse(V.is_contiguous(), 'V is contiguous')
torch.svd(X, out=(U, S, V))
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
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