def test_batch_eval_cosine8(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.zeros(2, DIMENSION, device=device, dtype=dtype)
res = cosine8(X)
self.assertEqual(res.dtype, dtype)
self.assertEqual(res.device.type, device.type)
self.assertEqual(res.shape, torch.Size([2]))
def test_cosine8_global_maximum(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.tensor(
GLOBAL_MAXIMIZER, device=device, dtype=dtype, requires_grad=True
)
res = cosine8(X)
self.assertAlmostEqual(res.item(), GLOBAL_MAXIMUM, places=4)
grad = torch.autograd.grad(res, X)[0]
self.assertLess(grad.abs().max().item(), 1e-4)