86ac9b94f772382087b4f92ebe0625173dfa314c,gpytorch/utils/linear_cg.py,,linear_cg,#Any#Any#Any#Any#Any#Any#Any#Any#Any#,11

Before Change

        // If residual are sufficiently small, then exit loop
        // Alternatively, exit if this is our last iteration
        torch.norm(residual, 2, dim=-2, out=residual_norm)
        if (residual_norm < tolerance).all() and not (n_tridiag and k < n_tridiag_iter):
            break

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

        // beta_{k} = (precon_residual{k}^T r_vec_{k}) / (precon_residual{k-1}^T r_vec_{k-1})
        residual_inner_prod.add_(eps)

After Change

                result,
                alpha,
                residual_inner_prod,
                torch.tensor(eps),
                beta,
                residual,
                precond_residual,

Instances