Remove redundant code from test_old_cholesky_batched

test/test_linalg.py

@@ -211,26 +211,10 @@ def cholesky_test_helper(n, batchsize, device, upper):
     @skipCPUIfNoLapack
     @dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
     def test_old_cholesky_batched(self, device, dtype):
-        from torch.testing._internal.common_utils import \
-            (random_symmetric_pd_matrix,
-             random_fullrank_matrix_distinct_singular_value)
+        from torch.testing._internal.common_utils import  random_hermitian_pd_matrix
 
         def cholesky_test_helper(n, batch_dims, upper):
-            # This is a workaround while there is no support for batched complex random_symmetric_pd_matrix
-            if dtype.is_complex:
-                real_dtype = torch.float32 if dtype is torch.complex64 else torch.float64
-                A_real = random_fullrank_matrix_distinct_singular_value(n, *batch_dims,
-                                                                        dtype=real_dtype, device=device)
-                A_imag = random_fullrank_matrix_distinct_singular_value(n, *batch_dims,
-                                                                        dtype=real_dtype, device=device)
-                A = A_real + 1j * A_imag
-                # There is no support for complex batched matmul yet
-                matmul_list = []
-                for mat in A.contiguous().view(-1, n, n):
-                    matmul_list.append(mat @ mat.t().conj())
-                A = torch.stack(matmul_list).view(*batch_dims, n, n)
-            else:
-                A = random_symmetric_pd_matrix(n, *batch_dims, dtype=dtype, device=device)
+            A = random_hermitian_pd_matrix(n, *batch_dims, dtype=dtype, device=device)
             cholesky_exp = torch.stack([m.cholesky(upper=upper) for m in A.reshape(-1, n, n)])
             cholesky_exp = cholesky_exp.reshape_as(A)
             self.assertEqual(cholesky_exp, torch.cholesky(A, upper=upper))