diff --git a/test/export/test_serialize.py b/test/export/test_serialize.py
index b4333a0d130f..8c8222d1b917 100644
--- a/test/export/test_serialize.py
+++ b/test/export/test_serialize.py
@@ -888,6 +888,42 @@ def forward(self, x):
         loaded_ep = load(buffer)
         self.assertEqual(m(*sample_inputs), loaded_ep.module()(*sample_inputs))
 
+    def test_1D_tensor_slicing(self) -> None:
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.const = torch.arange(8)[::2]
+
+            def forward(self, x):
+                return x + self.const
+
+        m = M()
+        sample_inputs = (torch.randn(4),)
+        ep = torch.export.export(m, sample_inputs)
+        buffer = io.BytesIO()
+        save(ep, buffer)
+        buffer.seek(0)
+        loaded_ep = load(buffer)
+        self.assertEqual(m(*sample_inputs), loaded_ep.module()(*sample_inputs))
+
+    def test_2D_tensor_slicing(self) -> None:
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.const = torch.randn(4, 4)[:2, :2]
+
+            def forward(self, x):
+                return x + self.const
+
+        m = M()
+        sample_inputs = (torch.randn(2, 2),)
+        ep = torch.export.export(m, sample_inputs)
+        buffer = io.BytesIO()
+        save(ep, buffer)
+        buffer.seek(0)
+        loaded_ep = load(buffer)
+        self.assertEqual(m(*sample_inputs), loaded_ep.module()(*sample_inputs))
+
     def test_complex_constant(self) -> None:
         class M(torch.nn.Module):
             def forward(self, x):
diff --git a/torch/export/pt2_archive/_package.py b/torch/export/pt2_archive/_package.py
index eab67a092e1c..f8849282dd9a 100644
--- a/torch/export/pt2_archive/_package.py
+++ b/torch/export/pt2_archive/_package.py
@@ -346,7 +346,7 @@ def _get_raw_tensor_bytes(value: torch.Tensor) -> bytes:
     if _is_fake_tensor(value):
         value_bytes = b""
     elif value.data_ptr():
-        cpu_tensor = value.cpu().contiguous()
+        cpu_tensor = value.cpu()
         value_untyped_storage = cpu_tensor.untyped_storage()
         # we store the raw bytes the untyped storage. Tensor metadata is stored separately
         value_bytes = bytes(