diff --git a/test/test_mps.py b/test/test_mps.py
index c59a598..d141e1a 100644
--- a/test/test_mps.py
+++ b/test/test_mps.py
@@ -270,6 +270,7 @@
         'empty_permuted',
         'empty_strided',
         'eye',
+        'exp',
         'expand',
         'expand_as',
         'flatten',
@@ -306,6 +307,7 @@
         'nn.functional.conv_transpose2d',
         'nn.functional.feature_alpha_dropoutwithout_train',
         'nn.functional.padcircular',
+        'nn.functional.tanhshrink',
         'nn.functional.unfold',
         'nonzero',
         'ones',
@@ -333,6 +335,7 @@
         'sub',
         'svd',
         't',
+        'tanh',
         'tensor_split',
         'transpose',
         'T',
@@ -389,7 +392,6 @@
         'eq',
         'equal',
         'exp2',
-        'exp',
         'expm1',
         'fft.fft',
         'fft.fft2',
@@ -447,7 +449,6 @@
         'nn.functional.pixel_unshuffle',
         'nn.functional.rms_norm',
         'nn.functional.softsign',
-        'nn.functional.tanhshrink',
         'pinverse',
         'prod',
         'reciprocal',
@@ -465,7 +466,6 @@
         'sum',
         'sum_to_size',
         'tan',
-        'tanh',
         'tensordot',
         'trace',
         'trapz',
@@ -1612,14 +1612,19 @@
 class TestMPS(TestCaseMPS):
     def test_exp(self, device="mps", dtype=torch.float):
         for v in (2, -2) + ((1j, 1 + 1j) if dtype.is_complex else ()):
-            b = torch.arange(18, device="cpu") / 3 * math.pi
-            a = torch.tensor(v, dtype=dtype, device="cpu") * b
-            a = a.to(dtype).to("mps")
+            b = torch.arange(18, dtype=dtype, device=device) / 3 * math.pi
+            a = torch.tensor(v, dtype=dtype, device="mps") * b
             self.compare_with_numpy(torch.exp, np.exp, a)
 
     def test_exp1(self, device="mps", dtype=torch.float):
-        input = torch.tensor([-0.1, 3.0, -0.9]).to('mps')
-        output = torch.exp(input).to('cpu')
+        input = torch.tensor([-0.1, 1.0, -0.9, 0.1], device=device, dtype=dtype)
+        output = torch.exp(input)
+        output_cpu = torch.exp(input.cpu())
+        # If exponentWithTensor: MPS call is used on M1 running 14.5 test will fail with
+        # Mismatched elements: 3 / 4 (75.0%)
+        # Greatest absolute difference: 1.1920928955078125e-07 at index (3,) (up to 1e-08 allowed)
+        # Greatest relative difference: 1.0786502002702036e-07 at index (3,) (up to 1e-08 allowed)
+        self.assertEqual(output, output_cpu, atol=1e-8, rtol=1e-8)
 
     def test_exp_strided_output(self):
         x = torch.rand((256, 10), device='mps')