diff --git a/aten/src/ATen/templates/Functions.cpp b/aten/src/ATen/templates/Functions.cpp
index c98bd80a792..baf835eb87b 100644
--- a/aten/src/ATen/templates/Functions.cpp
+++ b/aten/src/ATen/templates/Functions.cpp
@@ -49,6 +49,9 @@ Tensor TensorMaker::make_tensor() {
      } else {
        tensor_impl->set_sizes_contiguous(sizes_);
      }
+     if (storage_offset_) {
+       tensor_impl->set_storage_offset(*storage_offset_);
+     }
    }
 
    return tensor;
@@ -58,14 +61,22 @@ Tensor TensorMaker::make_tensor() {
    std::size_t itemsize = opts_.dtype().itemsize();
 
    if (strides_) {
-     return detail::computeStorageNbytes(sizes_, *strides_, itemsize);
+     auto storage_size = detail::computeStorageNbytes(sizes_, *strides_, itemsize);
+     if (storage_offset_) {
+       storage_size += storage_offset_.value();
+     }
+     return storage_size;
    }
 
    std::size_t size = 1;
    for (std::int64_t s : sizes_) {
      size *= static_cast<std::size_t>(s);
    }
-   return size * itemsize;
+   auto storage_size = size * itemsize;
+   if (storage_offset_) {
+     storage_size += storage_offset_.value();
+   }
+   return storage_size;
  }
 
  inline DataPtr TensorMaker::makeDataPtrFromDeleter() const {
diff --git a/test/test_python_dispatch.py b/test/test_python_dispatch.py
index 506739c3a0d..f40fd53a74f 100644
--- a/test/test_python_dispatch.py
+++ b/test/test_python_dispatch.py
@@ -298,6 +298,33 @@ $6 = torch._ops.aten.add_($1, $5)''')
         self.assertEqual(type(torch.full_like(MyTensor(2), 1.)), MyTensor)
         self.assertEqual(type(torch.randint_like(MyTensor(2), high=3)), MyTensor)
 
+    def test_make_wrapper_subclass_propagates_metadata(self) -> None:
+        class WrapperTensor(torch.Tensor):
+            elem: torch.Tensor
+
+            __slots__ = ['elem']
+
+            @staticmethod
+            def __new__(cls, elem, *args, **kwargs):
+                r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+                    cls, elem.size(),
+                    dtype=elem.dtype, layout=elem.layout,
+                    device=elem.device, requires_grad=elem.requires_grad,
+                    strides=elem.stride(), storage_offset=elem.storage_offset())
+                r.elem = elem
+                return r
+
+            @classmethod
+            def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+                raise RuntimeError("NYI")
+
+        # non-contiguous strides, non-zero storage offset
+        x = torch.randn(4, 6).t().diagonal(offset=2)
+        y = WrapperTensor(x)
+        self.assertEqual(y.size(), x.size())
+        self.assertEqual(y.stride(), x.stride())
+        self.assertEqual(y.storage_offset(), x.storage_offset())
+
     def test_enable_python_mode_error(self) -> None:
         with self.assertRaisesRegex(ValueError, "__torch_dispatch__"):
             with enable_python_mode(torch.Tensor):