def potrf_wrapper(A: torch.Tensor, clean: bool, upper: bool, use_cuda: bool,
opt: FalkonOptions) -> torch.Tensor:
if use_cuda:
from falkon.ooc_ops.ooc_potrf import gpu_cholesky
return gpu_cholesky(A,
upper=upper,
clean=clean,
overwrite=True,
opt=opt)
else:
return potrf(A, upper=upper, clean=clean, overwrite=True, cuda=False)
def test_potrf_speed():
t = 5000
mat = gen_random_pd(t, np.float32, F=False, seed=12345)
t_s = time.time()
our_chol = potrf(mat, upper=False, clean=True, overwrite=False, cuda=False)
our_time = time.time() - t_s
t_s = time.time()
np_chol = np.linalg.cholesky(mat)
np_time = time.time() - t_s
np.testing.assert_allclose(np_chol, our_chol, rtol=1e-5)
print("Time for cholesky(%d): Numpy %.2fs - Our %.2fs" %
(t, np_time, our_time))
def test_lower(self, mat, exp_lower, clean, overwrite, order, dtype):
mat = fix_mat(mat, order=order, dtype=dtype, copy=False, numpy=True)
inpt = mat.copy(order="K")
our_chol = potrf(inpt,
upper=False,
clean=clean,
overwrite=overwrite,
cuda=False)
if overwrite:
assert inpt.ctypes.data == our_chol.ctypes.data, "Overwriting failed"
if clean:
np.testing.assert_allclose(exp_lower,
our_chol,
rtol=self.rtol[dtype])
assert np.triu(our_chol, 1).sum() == 0
else:
np.testing.assert_allclose(exp_lower,
np.tril(our_chol),
rtol=self.rtol[dtype])
np.testing.assert_allclose(np.triu(mat, 1), np.triu(our_chol, 1))