fix clone for multi (#14919)

also update empty_like to make sure it's backed by buffers

test/backend/test_multitensor.py

@@ -840,13 +840,15 @@ class TestMultiTensor(unittest.TestCase):
     t.shard_(devices, axis=0).realize()
     assert all([lb is lb.base and lb.realized.base.size == 4 * 16 for lb in t.uop.src])
 
-  @unittest.skip("this is unreliable on OSX")
   def test_clone(self):
-    t = Tensor.rand(16, 16).shard(devices_2, axis=None)
-    np.testing.assert_allclose(t.numpy(), t.clone().numpy())
-
-    t = Tensor.rand(16, 16).shard(devices_2, axis=0)
-    np.testing.assert_allclose(t.numpy(), t.clone().numpy())
+    for axis in (None, 0):
+      t = Tensor.arange(16).reshape(4, 4).shard(devices_2, axis=axis).contiguous().realize()
+      t_clone = t.clone().realize()
+      self.assertEqual(t_clone.device, t.device)
+      self.assertEqual(t_clone.uop.axis, axis)
+      self.assertEqual(t_clone.tolist(), t.tolist())
+      t_clone += 1
+      self.assertNotEqual(t_clone.tolist(), t.tolist())
 
   @unittest.skip("RANGEIFY doesn't support multi const folding")
   def test_multi_const_folding(self):

test/null/test_multitensor.py

@@ -72,6 +72,7 @@ class TestMultiAxis(unittest.TestCase):
     self.assertEqual(e.shape, t.shape)
     self.assertEqual(e.device, t.device)
     self.assertEqual(e.uop.axis, 0)
+    self.assertTrue(e.uop.has_buffer_identity())
 
 if __name__ == '__main__':
   unittest.main()

tinygrad/tensor.py

@@ -404,7 +404,7 @@ class Tensor(OpMixin):
     """
     Creates a clone of this tensor allocating a separate buffer for the data.
     """
-    ret = Tensor.empty(self.shape, device=self.device, dtype=self.dtype)
+    ret = self.empty_like()
     if self.grad is not None: ret.grad = self.grad.clone()
     return ret.assign(self)
 
@@ -544,7 +544,7 @@ class Tensor(OpMixin):
     """
     dtype, device = self.dtype if dtype is None else dtype, self.device if device is None else device
     if isinstance(device, tuple) and (axis := self.uop.axis) is not None:
-      return Tensor.empty(self.shape, dtype=dtype, device=device[0], **kwargs).shard(device, axis)
+      return Tensor(Tensor.empty(self.uop.max_shard_shape, dtype=dtype, device=device, **kwargs).uop.multi(axis), device=device)
     return Tensor.empty(self.shape, dtype=dtype, device=device, **kwargs)
 
   @staticmethod