9233f51a05def9f3b2106231f7089295fb205ddc,gpytorch/variational/whitened_variational_strategy.py,WhitenedVariationalStrategy,forward,#WhitenedVariationalStrategy#Any#,67

Before Change

                    induc_data_covar.transpose(-1, -2), predictive_covar @ induc_data_covar
                )

            if beta_features.diagonal_correction.on():
                diag_correction = (data_data_covar.diag() - interp_data_data_var).clamp(0, math.inf)
                predictive_covar = DiagLazyTensor(predictive_covar.diag() + diag_correction)

            // Save the logdet, mean_diff_inv_quad, prior distribution for the ELBO
            if self.training:
                self._memoize_cache["prior_distribution_memo"] = MultivariateNormal(induc_mean, induc_induc_covar)
                self._memoize_cache["logdet_memo"] = -logdet
                self._memoize_cache["mean_diff_inv_quad_memo"] = mean_diff_inv_quad

After Change

                    induc_data_covar.transpose(-1, -2), predictive_covar @ induc_data_covar
                )

            if self.training:
                data_covariance = DiagLazyTensor((data_data_covar.diag() - interp_data_data_var).clamp(0, math.inf))
            else:
                neg_induc_data_data_covar = induc_induc_covar.inv_matmul(
                    induc_data_covar, left_tensor=induc_data_covar.transpose(-1, -2).mul(-1)
                )
                data_covariance = data_data_covar + neg_induc_data_data_covar
            predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)

            // Save the logdet, mean_diff_inv_quad, prior distribution for the ELBO
            if self.training:

Instances