def test_inv_matmul(self):
base_lazy_variable_mat = torch.randn(6, 6)
base_lazy_variable_mat = base_lazy_variable_mat.t().matmul(
base_lazy_variable_mat)
test_matrix = torch.randn(3, 4)
left_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4],
[4, 5]]),
requires_grad=True)
left_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1], [1, 3]]),
requires_grad=True)
right_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4],
[4, 5]]),
requires_grad=True)
right_interp_values = Variable(torch.Tensor([[1, 2], [0.5, 1], [1,
3]]),
requires_grad=True)
left_interp_values_copy = Variable(left_interp_values.data,
requires_grad=True)
right_interp_values_copy = Variable(right_interp_values.data,
requires_grad=True)
base_lazy_variable = Variable(base_lazy_variable_mat,
requires_grad=True)
base_lazy_variable_copy = Variable(base_lazy_variable_mat,
requires_grad=True)
test_matrix_var = Variable(test_matrix, requires_grad=True)
test_matrix_var_copy = Variable(test_matrix, requires_grad=True)
interp_lazy_var = InterpolatedLazyVariable(
NonLazyVariable(base_lazy_variable),
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_var.inv_matmul(test_matrix_var)
left_matrix = Variable(torch.zeros(3, 6))
right_matrix = Variable(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_variable_copy).matmul(
right_matrix.transpose(-1, -2))
actual = gpytorch.inv_matmul(actual_mat, test_matrix_var_copy)
self.assertTrue(approx_equal(res.data, actual.data))
# Backward pass
res.sum().backward()
actual.sum().backward()
self.assertTrue(
approx_equal(base_lazy_variable.grad.data,
base_lazy_variable_copy.grad.data))
self.assertTrue(
approx_equal(left_interp_values.grad.data,
left_interp_values_copy.grad.data))
def test_inv_matmul_batch(self):
base_lazy_variable_mat = torch.randn(6, 6)
base_lazy_variable_mat = ((base_lazy_variable_mat.t().matmul(
base_lazy_variable_mat)).unsqueeze(0).repeat(5, 1, 1))
test_matrix = torch.randn(5, 3, 4)
left_interp_indices = Variable(torch.LongTensor(
[[2, 3], [3, 4], [4, 5]]).unsqueeze(0).repeat(5, 1, 1),
requires_grad=True)
left_interp_values = Variable(torch.Tensor(
[[1, 2], [0.5, 1], [1, 3]]).unsqueeze(0).repeat(5, 1, 1),
requires_grad=True)
right_interp_indices = Variable(torch.LongTensor(
[[2, 3], [3, 4], [4, 5]]).unsqueeze(0).repeat(5, 1, 1),
requires_grad=True)
right_interp_values = Variable(torch.Tensor(
[[1, 2], [0.5, 1], [1, 3]]).unsqueeze(0).repeat(5, 1, 1),
requires_grad=True)
left_interp_values_copy = Variable(left_interp_values.data,
requires_grad=True)
right_interp_values_copy = Variable(right_interp_values.data,
requires_grad=True)
base_lazy_variable = Variable(base_lazy_variable_mat,
requires_grad=True)
base_lazy_variable_copy = Variable(base_lazy_variable_mat,
requires_grad=True)
test_matrix_var = Variable(test_matrix, requires_grad=True)
test_matrix_var_copy = Variable(test_matrix, requires_grad=True)
interp_lazy_var = InterpolatedLazyVariable(
NonLazyVariable(base_lazy_variable),
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
res = interp_lazy_var.inv_matmul(test_matrix_var)
left_matrix_comps = []
right_matrix_comps = []
for i in range(5):
left_matrix_comp = Variable(torch.zeros(3, 6))
right_matrix_comp = Variable(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_variable_copy).matmul(
right_matrix.transpose(-1, -2))
actual = gpytorch.inv_matmul(actual_mat, test_matrix_var_copy)
self.assertTrue(approx_equal(res.data, actual.data))
# Backward pass
res.sum().backward()
actual.sum().backward()
self.assertTrue(
approx_equal(base_lazy_variable.grad.data,
base_lazy_variable_copy.grad.data))
self.assertTrue(
approx_equal(left_interp_values.grad.data,
left_interp_values_copy.grad.data))