make paddle.to_tensor() copy if data is varbase (#33335)

* fix stop_gradient in paddle.to_tensor

* make to_tensor copy if data is varbase

* add ut

* refine sample code

python/paddle/fluid/tests/unittests/test_var_base.py

@@ -230,6 +230,14 @@ def _test_place(place):
             _test_place(core.CUDAPlace(0))
             _test_place("gpu:0")
 
+    def test_to_tensor_not_change_input_stop_gradient(self):
+        with paddle.fluid.dygraph.guard(core.CPUPlace()):
+            a = paddle.zeros([1024])
+            a.stop_gradient = False
+            b = paddle.to_tensor(a)
+            self.assertEqual(a.stop_gradient, False)
+            self.assertEqual(b.stop_gradient, True)
+
     def test_to_tensor_change_place(self):
         if core.is_compiled_with_cuda():
             a_np = np.random.rand(1024, 1024)
@@ -260,8 +268,9 @@ def test_to_tensor_with_lodtensor(self):
             with paddle.fluid.dygraph.guard(core.CUDAPlace(0)):
                 lod_tensor = core.LoDTensor()
                 lod_tensor.set(a_np, core.CUDAPlace(0))
-                a = paddle.to_tensor(lod_tensor)
+                a = paddle.to_tensor(lod_tensor, place=core.CPUPlace())
                 self.assertTrue(np.array_equal(a_np, a.numpy()))
+                self.assertTrue(a.place.__repr__(), "CPUPlace")
 
     def test_to_variable(self):
         with fluid.dygraph.guard():

python/paddle/tensor/creation.py

@@ -40,9 +40,8 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
     Constructs a ``paddle.Tensor`` from ``data`` , 
     which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor.
 
-    If the ``data`` is already a tensor, and ``dtype`` or ``place`` does't change, no copy 
-    will be performed and return origin tensor, otherwise a new tensor will be constructed
-    and returned. 
+    If the ``data`` is already a Tensor, copy will be performed and return a new tensor.
+    If you only want to change stop_gradient property, please call ``Tensor.stop_gradient = stop_gradient`` directly.
 
     Args:
         data(scalar|tuple|list|ndarray|Tensor): Initial data for the tensor.
@@ -75,32 +74,31 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
         # <class 'paddle.Tensor'>
 
         paddle.to_tensor(1)
-        # Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
+        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
         #        [1])
 
-        x = paddle.to_tensor(1)
-        paddle.to_tensor(x, dtype='int32', place=paddle.CPUPlace()) # A new tensor will be constructed due to different dtype or place
-        # Tensor(shape=[1], dtype=int32, place=CPUPlace, stop_gradient=True,
+        x = paddle.to_tensor(1, stop_gradient=False)
+        print(x)
+        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=False,
         #        [1])
 
-        paddle.to_tensor((1.1, 2.2), place=paddle.CUDAPinnedPlace())
-        # Tensor(shape=[1], dtype=float32, place=CUDAPinnedPlace, stop_gradient=True,
-        #        [1])
+        paddle.to_tensor(x)  # A new tensor will be created with default stop_gradient=True
+        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
+        #        [1])        
 
-        paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CUDAPlace(0), stop_gradient=False)
-        # Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
+        paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False)
+        # Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
         #        [[0.10000000, 0.20000000],
         #         [0.30000001, 0.40000001]])
 
         type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
-        # <class 'paddle.VarBase'>
+        # <class 'paddle.Tensor'>
 
         paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
-        # Tensor(shape=[2, 2], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
+        # Tensor(shape=[2, 2], dtype=complex64, place=CPUPlace, stop_gradient=True,
         #        [[(1+1j), (2+0j)],
         #         [(3+2j), (4+0j)]])
     """
-
     place = _get_paddle_place(place)
     if place is None:
         place = _current_expected_place()
@@ -119,10 +117,7 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
 
     if not isinstance(data, np.ndarray):
 
-        def _handle_diff_place_dtype(data, dtype, place, stop_gradient):
-            data.stop_gradient = stop_gradient
-            if not data.place._equals(place):
-                data = data._copy_to(place, False)
+        def _handle_dtype(data, dtype):
             if dtype:
                 if convert_dtype(dtype) != convert_dtype(data.dtype):
                     return data.astype(convert_dtype(dtype))
@@ -138,11 +133,17 @@ def _handle_diff_place_dtype(data, dtype, place, stop_gradient):
                     "this means the input data contains nested lists with different lengths. "
                 )
         elif isinstance(data, paddle.Tensor):
-            return _handle_diff_place_dtype(data, dtype, place, stop_gradient)
-        elif isinstance(data, (core.Tensor, core.LoDTensor)):
-            # convert LoDTensor to VarBase first, and then process it as input VarBase
+            data = data._copy_to(place, False)
+            ata = _handle_dtype(data, dtype)
+            data.stop_gradient = stop_gradient
+        elif isinstance(data, core.LoDTensor):
+            # convert LoDTensor to VarBase first
+            # Currenly, LoDTensor does no copy when places are same
             data = paddle.Tensor(data)
-            return _handle_diff_place_dtype(data, dtype, place, stop_gradient)
+            if not data.place._equals(place):
+                data = data._copy_to(place, False)
+            data = _handle_dtype(data, dtype)
+            data.stop_gradient = stop_gradient
         else:
             raise TypeError(
                 "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor".