a1e8bcc26a4b432776620b242023adb7acf206e3,gpytorch/utils/linear_cg.py,,linear_cg,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,11

Before Change

        // beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
        residual_inner_prod.add_(eps)
        torch.reciprocal(residual_inner_prod, out=beta)
        torch.mul(residual, precond_residual, out=mul_storage)
        torch.sum(mul_storage, -2, keepdim=True, out=residual_inner_prod)
        beta.mul_(residual_inner_prod)

        // Update curr_conjugate_vec
        // curr_conjugate_vec_{k} = precon_residual{k} + beta_{k} curr_conjugate_vec_{k-1}
        curr_conjugate_vec.mul_(beta).add_(precond_residual)

        // Update tridiagonal matrices, if applicable
        if n_tridiag and k < n_tridiag_iter and update_tridiag:
            alpha_tridiag = alpha.squeeze_(-2).narrow(-1, 0, n_tridiag)

After Change

            // precon_residual{k} = M^-1 residual_{k}
            precond_residual = preconditioner(residual)

            _jit_linear_cg_updates(
                result,
                alpha,
                residual_inner_prod,
                torch.tensor(eps),
                beta,
                residual,
                precond_residual,
                mul_storage,
                curr_conjugate_vec,
            )
        else:
            _jit_linear_cg_updates_no_precond(
                mvms,
                result,

Instances