mtgl returns sumkroneckerlt

Author: wjmaddox

gpytorch/lazy/sum_kronecker_lazy_tensor.py

@@ -75,7 +75,7 @@ def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True)
         logdet_term = None
 
         if inv_quad_rhs is not None:
-            solve = self._solve(inv_quad_rhs)
+            solve = self.inv_matmul(inv_quad_rhs)
             inv_quad_term = (inv_quad_rhs * solve).sum(-2)
 
             if inv_quad_term.numel() and reduce_inv_quad:

gpytorch/likelihoods/multitask_gaussian_likelihood.py

@@ -12,6 +12,7 @@
     DiagLazyTensor,
     KroneckerProductDiagLazyTensor,
     KroneckerProductLazyTensor,
+    LazyEvaluatedKernelTensor,
     RootLazyTensor,
 )
 from ..likelihoods import Likelihood, _GaussianLikelihoodBase
@@ -88,6 +89,10 @@ def marginal(self, function_dist, *params, **kwargs):
         """
         mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix
 
+        # ensure that sumKroneckerLT is actually called
+        if isinstance(covar, LazyEvaluatedKernelTensor):
+            covar = covar.evaluate_kernel()
+
         covar_kron_lt = self._shaped_noise_covar(mean.shape, add_noise=self.has_global_noise)
         covar = covar + covar_kron_lt
 
@@ -126,7 +131,7 @@ def _shaped_noise_covar(self, shape, add_noise=True, *params, **kwargs):
 
     def forward(self, function_samples: Tensor, *params: Any, **kwargs: Any) -> base_distributions.Normal:
         noise = self._shaped_noise_covar(function_samples.shape, *params, **kwargs).diag()
-        noise = noise.view(*noise.shape[:-1], *function_samples.shape[-2:])
+        noise = noise.reshape(*noise.shape[:-1], *function_samples.shape[-2:])
         return base_distributions.Independent(base_distributions.Normal(function_samples, noise.sqrt()), 1)