Merge pull request #72 from Turakar/fix_masked_t_matmul

Fix _t_matmul() in MaskedLinearOperator and add test

linear_operator/operators/masked_linear_operator.py

@@ -50,7 +50,7 @@ def _matmul(
         rhs: Union[Float[torch.Tensor, "*batch2 N C"], Float[torch.Tensor, "*batch2 N"]],
     ) -> Union[Float[torch.Tensor, "... M C"], Float[torch.Tensor, "... M"]]:
         rhs_expanded = self._expand(rhs, self.col_mask)
-        res_expanded = self.base.matmul(rhs_expanded)
+        res_expanded = self.base._matmul(rhs_expanded)
         res = res_expanded[..., self.row_mask, :]
 
         return res
@@ -60,7 +60,7 @@ def _t_matmul(
         rhs: Union[Float[Tensor, "*batch2 M P"], Float[LinearOperator, "*batch2 M P"]],
     ) -> Union[Float[LinearOperator, "... N P"], Float[Tensor, "... N P"]]:
         rhs_expanded = self._expand(rhs, self.row_mask)
-        res_expanded = self.base.t_matmul(rhs_expanded)
+        res_expanded = self.base._t_matmul(rhs_expanded)
         res = res_expanded[..., self.col_mask, :]
         return res
 

linear_operator/test/linear_operator_test_case.py

@@ -391,6 +391,20 @@ def test_matmul_matrix(self):
         rhs = torch.randn(*linear_op.batch_shape, linear_op.size(-1), 4)
         return self._test_matmul(rhs)
 
+    def test_t_matmul_matrix(self):
+        with torch.no_grad():
+            linear_op = self.create_linear_op()
+            rhs = torch.randn(*linear_op.batch_shape, linear_op.size(-2), 4)
+            linear_op_copy = torch.clone(linear_op)
+            evaluated = self.evaluate_linear_op(linear_op_copy)
+            rhs_evaluated = to_dense(rhs)
+
+            # Test operator
+            res = linear_op._t_matmul(rhs)
+            actual = evaluated.mT.matmul(rhs_evaluated)
+            res_evaluated = to_dense(res)
+            self.assertAllClose(res_evaluated, actual)
+
     def test_rmatmul_matrix(self):
         linear_op = self.create_linear_op()
         lhs = torch.randn(*linear_op.batch_shape, 4, linear_op.size(-2))