531bc80bae88a5f240583bc19de9fc4f65e675a5,gpytorch/math/functions/interpolated_toeplitz_gp_marginal_log_likelihood.py,InterpolatedToeplitzGPMarginalLogLikelihood,backward,#InterpolatedToeplitzGPMarginalLogLikelihood#Any#,49

Before Change

            zeros = torch.zeros(n)
            for i in range(n):
                e_i = zeros.copy()
                e_i[i] = 1
                mat_inv_ei = LinearCG().solve(mv_closure, e_i)
                trace += mat_inv_ei[i]

After Change

        if self.needs_input_grad[2]:
            n = len(y)
            quad_form_part = mat_inv_y.dot(mat_inv_y)
            noise_grad = c.new().resize_(1).fill_(quad_form_part - self.tr_inv).mul_(0.5 * grad_output_value)

        return mat_grad, y_grad, noise_grad

Instances