gpytorch/variational/cholesky_variational_distribution.py

Killed 16 out of 19 mutants

Survived

Survived mutation testing. These mutants show holes in your test suite.

Mutant 310

--- gpytorch/variational/cholesky_variational_distribution.py
+++ gpytorch/variational/cholesky_variational_distribution.py
@@ -35,7 +35,7 @@
 
     def forward(self):
         chol_variational_covar = self.chol_variational_covar
-        dtype = chol_variational_covar.dtype
+        dtype = None
         device = chol_variational_covar.device
 
         # First make the cholesky factor is upper triangular

Mutant 311

--- gpytorch/variational/cholesky_variational_distribution.py
+++ gpytorch/variational/cholesky_variational_distribution.py
@@ -36,7 +36,7 @@
     def forward(self):
         chol_variational_covar = self.chol_variational_covar
         dtype = chol_variational_covar.dtype
-        device = chol_variational_covar.device
+        device = None
 
         # First make the cholesky factor is upper triangular
         lower_mask = torch.ones(self.chol_variational_covar.shape[-2:], dtype=dtype, device=device).tril(0)

Mutant 313

--- gpytorch/variational/cholesky_variational_distribution.py
+++ gpytorch/variational/cholesky_variational_distribution.py
@@ -39,7 +39,7 @@
         device = chol_variational_covar.device
 
         # First make the cholesky factor is upper triangular
-        lower_mask = torch.ones(self.chol_variational_covar.shape[-2:], dtype=dtype, device=device).tril(0)
+        lower_mask = torch.ones(self.chol_variational_covar.shape[-3:], dtype=dtype, device=device).tril(0)
         chol_variational_covar = TriangularLazyTensor(chol_variational_covar.mul(lower_mask))
 
         # Now construct the actual matrix