diff --git a/test/test_dynamic_shapes.py b/test/test_dynamic_shapes.py
index efa1875afdf4..6a721a079a63 100644
--- a/test/test_dynamic_shapes.py
+++ b/test/test_dynamic_shapes.py
@@ -3187,9 +3187,10 @@ class TestUbackedOps(TestCase):
             f = y.item()
             t1 = x.view((f, f))
             t2 = x.reshape((f, f))
+            t3 = torch._ops.ops.aten.view_copy(x, (f, f))
             # TODO avoid _check_is_size here.
             torch._check_is_size(f)
-            return t1 * 10, t2 * 10
+            return t1 * 10, t2 * 10, t3
 
         compiled_func = torch.compile(
             fullgraph=True,
@@ -3229,10 +3230,12 @@ def forward(self, arg0_1: "i64[1][1]cpu", arg1_1: "Sym(u1)", arg2_1: "Sym(s7)",
         eq: "Sym(Eq(u1, u0**2))" = arg1_1 == pow_1;  arg1_1 = pow_1 = None
         _assert_scalar_2 = torch.ops.aten._assert_scalar.default(eq, "Runtime assertion failed for expression Eq(u1, u0**2) on node 'eq'");  eq = _assert_scalar_2 = None
         view: "i64[u0, u0][s7*u0, s7]cpu" = torch.ops.aten.view.default(arg3_1, [_local_scalar_dense, _local_scalar_dense])
-        view_1: "i64[u0, u0][s7*u0, s7]cpu" = torch.ops.aten.view.default(arg3_1, [_local_scalar_dense, _local_scalar_dense]);  arg3_1 = _local_scalar_dense = None
-        mul_9: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view, 10);  view = None
-        mul_12: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view_1, 10);  view_1 = None
-        return (mul_9, mul_12)""",  # noqa: B950
+        view_1: "i64[u0, u0][s7*u0, s7]cpu" = torch.ops.aten.view.default(arg3_1, [_local_scalar_dense, _local_scalar_dense])
+        view_2: "i64[u0, u0][s7*u0, s7]cpu" = torch.ops.aten.view.default(arg3_1, [_local_scalar_dense, _local_scalar_dense]);  arg3_1 = _local_scalar_dense = None
+        clone: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.clone.default(view_2);  view_2 = None
+        mul_11: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view, 10);  view = None
+        mul_14: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view_1, 10);  view_1 = None
+        return (mul_11, mul_14, clone)""",  # noqa: B950
             ignore_comments=True,
             ignore_empty_lines=True,
         )
@@ -3268,10 +3271,12 @@ def forward(self, arg0_1: "i64[1][1]cpu", arg1_1: "Sym(u1)", arg2_1: "i64[u1][1]
         eq: "Sym(Eq(u1, u0**2))" = arg1_1 == pow_1;  arg1_1 = pow_1 = None
         _assert_scalar_2 = torch.ops.aten._assert_scalar.default(eq, "Runtime assertion failed for expression Eq(u1, u0**2) on node 'eq'");  eq = _assert_scalar_2 = None
         view: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.view.default(arg2_1, [_local_scalar_dense, _local_scalar_dense])
-        view_1: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.view.default(arg2_1, [_local_scalar_dense, _local_scalar_dense]);  arg2_1 = _local_scalar_dense = None
-        mul_4: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view, 10);  view = None
-        mul_7: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view_1, 10);  view_1 = None
-        return (mul_4, mul_7)""",  # noqa: B950
+        view_1: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.view.default(arg2_1, [_local_scalar_dense, _local_scalar_dense])
+        view_2: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.view.default(arg2_1, [_local_scalar_dense, _local_scalar_dense]);  arg2_1 = _local_scalar_dense = None
+        clone: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.clone.default(view_2);  view_2 = None
+        mul_6: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view, 10);  view = None
+        mul_9: "i64[u0, u0][Max(1, u0), 1]cpu" = torch.ops.aten.mul.Tensor(view_1, 10);  view_1 = None
+        return (mul_6, mul_9, clone)""",  # noqa: B950
             ignore_comments=True,
             ignore_empty_lines=True,
         )
diff --git a/torch/_subclasses/fake_impls.py b/torch/_subclasses/fake_impls.py
index 7348c3b1c0b4..4d33280f7ac8 100644
--- a/torch/_subclasses/fake_impls.py
+++ b/torch/_subclasses/fake_impls.py
@@ -601,6 +601,18 @@ def _view_meta(fake_mode, func, a, *shape):
         return torch._refs._reshape_view_helper(a, *shape, allow_copy=False)
 
 
+@register_op_impl(aten.view_copy.default)
+def _view_meta_copy(fake_mode, func, a, *shape, out=None):
+    result = _view_meta(fake_mode, func, a, *shape)
+    if out is not None:
+        return result
+
+    return pytree.tree_map(
+        lambda x: x.clone(memory_format=torch.contiguous_format),
+        result,
+    )
+
+
 @register_op_impl(aten.repeat_interleave.Tensor)
 def repeat_interleave_tensor(fake_mode, func, repeats, output_size=None):
     if output_size is None: