def test_inv_matmul(self):
base_lazy_tensor_mat = torch.randn(6, 6)
base_lazy_tensor_mat = base_lazy_tensor_mat.t().matmul(
base_lazy_tensor_mat)
test_matrix = torch.randn(3, 4)
left_interp_indices = torch.LongTensor([[2, 3], [3, 4], [4, 5]])
left_interp_values = torch.tensor([[1, 2], [0.5, 1], [1, 3]],
dtype=torch.float)
left_interp_values_copy = left_interp_values.clone()
left_interp_values.requires_grad = True
left_interp_values_copy.requires_grad = True
right_interp_indices = torch.LongTensor([[2, 3], [3, 4], [4, 5]])
right_interp_values = torch.tensor([[1, 2], [0.5, 1], [1, 3]],
dtype=torch.float)
right_interp_values_copy = right_interp_values.clone()
right_interp_values.requires_grad = True
right_interp_values_copy.requires_grad = True
base_lazy_tensor = base_lazy_tensor_mat
base_lazy_tensor.requires_grad = True
base_lazy_tensor_copy = base_lazy_tensor_mat
test_matrix_tensor = test_matrix
test_matrix_tensor.requires_grad = True
test_matrix_tensor_copy = test_matrix
interp_lazy_tensor = InterpolatedLazyTensor(
NonLazyTensor(base_lazy_tensor),
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_tensor.inv_matmul(test_matrix_tensor)
left_matrix = torch.zeros(3, 6)
right_matrix = torch.zeros(3, 6)
left_matrix.scatter_(1, left_interp_indices, left_interp_values_copy)
right_matrix.scatter_(1, right_interp_indices,
right_interp_values_copy)
actual_mat = left_matrix.matmul(base_lazy_tensor_copy).matmul(
right_matrix.transpose(-1, -2))
actual = gpytorch.inv_matmul(actual_mat, test_matrix_tensor_copy)
self.assertTrue(approx_equal(res, actual))
# Backward pass
res.sum().backward()
actual.sum().backward()
self.assertTrue(
approx_equal(base_lazy_tensor.grad, base_lazy_tensor_copy.grad))
self.assertTrue(
approx_equal(left_interp_values.grad,
left_interp_values_copy.grad))
def test_inv_matmul_batch(self):
base_lazy_tensor = torch.randn(6, 6)
base_lazy_tensor = (
base_lazy_tensor.t().matmul(base_lazy_tensor)).unsqueeze(0).repeat(
5, 1, 1)
base_lazy_tensor_copy = base_lazy_tensor.clone()
base_lazy_tensor.requires_grad = True
base_lazy_tensor_copy.requires_grad = True
test_matrix_tensor = torch.randn(5, 3, 4)
test_matrix_tensor_copy = test_matrix_tensor.clone()
test_matrix_tensor.requires_grad = True
test_matrix_tensor_copy.requires_grad = True
left_interp_indices = torch.LongTensor([[2, 3], [3, 4],
[4, 5]]).unsqueeze(0).repeat(
5, 1, 1)
left_interp_values = torch.tensor(
[[1, 2], [0.5, 1], [1, 3]],
dtype=torch.float).unsqueeze(0).repeat(5, 1, 1)
left_interp_values_copy = left_interp_values.clone()
left_interp_values.requires_grad = True
left_interp_values_copy.requires_grad = True
right_interp_indices = torch.LongTensor([[2, 3], [3, 4],
[4, 5]]).unsqueeze(0).repeat(
5, 1, 1)
right_interp_values = torch.tensor(
[[1, 2], [0.5, 1], [1, 3]],
dtype=torch.float).unsqueeze(0).repeat(5, 1, 1)
right_interp_values_copy = right_interp_values.clone()
right_interp_values.requires_grad = True
right_interp_values_copy.requires_grad = True
interp_lazy_tensor = InterpolatedLazyTensor(
NonLazyTensor(base_lazy_tensor),
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_tensor.inv_matmul(test_matrix_tensor)
left_matrix_comps = []
right_matrix_comps = []
for i in range(5):
left_matrix_comp = torch.zeros(3, 6)
right_matrix_comp = torch.zeros(3, 6)
left_matrix_comp.scatter_(1, left_interp_indices[i],
left_interp_values_copy[i])
right_matrix_comp.scatter_(1, right_interp_indices[i],
right_interp_values_copy[i])
left_matrix_comps.append(left_matrix_comp.unsqueeze(0))
right_matrix_comps.append(right_matrix_comp.unsqueeze(0))
left_matrix = torch.cat(left_matrix_comps)
right_matrix = torch.cat(right_matrix_comps)
actual_mat = left_matrix.matmul(base_lazy_tensor_copy).matmul(
right_matrix.transpose(-1, -2))
actual = gpytorch.inv_matmul(actual_mat, test_matrix_tensor_copy)
self.assertTrue(approx_equal(res, actual))
# Backward pass
res.sum().backward()
actual.sum().backward()
self.assertTrue(
approx_equal(base_lazy_tensor.grad, base_lazy_tensor_copy.grad))
self.assertTrue(
approx_equal(left_interp_values.grad,
left_interp_values_copy.grad))
