# Buggy code
def kl_divergence(self):
    variational_dist_u = self.variational_distribution.variational_distribution
    prior_dist = self.prior_distribution
    kl_divergence = 0.5 * sum(
        [
            # log|k| - log|S|
            # = log|K| - log|K var_dist_covar K|
            # = -log|K| - log|var_dist_covar|
            self.prior_covar_logdet(),
            -variational_dist_u.lazy_covariance_matrix.logdet(),
            # tr(K^-1 S) = tr(K^1 K var_dist_covar K) = tr(K var_dist_covar)
            self.covar_trace(),
            # (m - \mu u)^T K^-1 (m - \mu u)
            # = (K^-1 (m - \mu u)) K (K^1 (m - \mu u))
            # = (var_dist_mean)^T K (var_dist_mean)
            self.mean_diff_inv_quad(),
            # d
            -prior_dist.event_shape.numel(),
        ]
    )

    return kl_divergence


# Traceback (AttributeError)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5     for x_batch, y_batch in test_loader:
----> 6         preds = model(x_batch)
7         means = torch.cat([means, preds.mean.cpu()])
8 means = means[1:]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/models/abstract_variational_gp.py in __call__(self, inputs, **kwargs)
20             inputs = inputs.unsqueeze(-1)
21
---> 22         return self.variational_strategy(inputs)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in __call__(self, x)
228                 self._memoize_cache = dict()
229
--> 230         return super(VariationalStrategy, self).__call__(x)

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/module.py in __call__(self, *inputs, **kwargs)
20
21     def __call__(self, *inputs, **kwargs):
---> 22         outputs = self.forward(*inputs, **kwargs)
23         if isinstance(outputs, list):
24             return [_validate_module_outputs(output) for output in outputs]

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/variational/variational_strategy.py in forward(self, x)
214                     induc_induc_covar.inv_matmul(induc_data_covar)
215                 )
--> 216                 data_covariance = data_data_covar + neg_induc_data_data_covar
217             predictive_covar = PsdSumLazyTensor(predictive_covar, data_covariance)
218

~/miniconda3/envs/mbrl/lib/python3.7/site-packages/gpytorch/lazy/sum_lazy_tensor.py in __add__(self, other)
75             return SumLazyTensor(*(list(self.lazy_tensors) + [other]))
76         else:
---> 77             raise AttributeError("other must be a LazyTensor")
78
79     def diag(self):

AttributeError: other must be a LazyTensor

# Fixed code
def kl_divergence(self):
    variational_dist_u = self.variational_distribution
    prior_dist = self.prior_distribution
    kl_divergence = 0.5 * sum(
        [
            # log|k| - log|S|
            # = log|K| - log|K var_dist_covar K|
            # = -log|K| - log|var_dist_covar|
            self.prior_covar_logdet(),
            -variational_dist_u.lazy_covariance_matrix.logdet(),
            # tr(K^-1 S) = tr(K^1 K var_dist_covar K) = tr(K var_dist_covar)
            self.covar_trace(),
            # (m - \mu u)^T K^-1 (m - \mu u)
            # = (K^-1 (m - \mu u)) K (K^1 (m - \mu u))
            # = (var_dist_mean)^T K (var_dist_mean)
            self.mean_diff_inv_quad(),
            # d
            -prior_dist.event_shape.numel(),
        ]
    )

    return kl_divergence