paddle.to_tensor supports LoDTensor

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

@@ -248,6 +248,21 @@ def test_to_tensor_change_place(self):
                 a = paddle.to_tensor(a, place=paddle.CUDAPinnedPlace())
                 self.assertEqual(a.place.__repr__(), "CUDAPinnedPlace")
 
+    def test_to_tensor_with_lodtensor(self):
+        if core.is_compiled_with_cuda():
+            a_np = np.random.rand(1024, 1024)
+            with paddle.fluid.dygraph.guard(core.CPUPlace()):
+                lod_tensor = core.LoDTensor()
+                lod_tensor.set(a_np, core.CPUPlace())
+                a = paddle.to_tensor(lod_tensor)
+                self.assertTrue(np.array_equal(a_np, a.numpy()))
+
+            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)
+                self.assertTrue(np.array_equal(a_np, a.numpy()))
+
     def test_to_variable(self):
         with fluid.dygraph.guard():
             var = fluid.dygraph.to_variable(self.array, name="abc")

python/paddle/tensor/creation.py

@@ -118,6 +118,16 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
         place = _current_expected_place()
 
     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)
+            if dtype:
+                if convert_dtype(dtype) != convert_dtype(data.dtype):
+                    return data.astype(convert_dtype(dtype))
+            return data
+
         if np.isscalar(data) and not isinstance(data, str):
             data = np.array([data])
         elif isinstance(data, (list, tuple)):
@@ -128,13 +138,11 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
                     "this means the input data contains nested lists with different lengths. "
                 )
         elif isinstance(data, paddle.Tensor):
-            data.stop_gradient = stop_gradient
-            if not data.place._equals(place):
-                data = data._copy_to(place, False)
-            if dtype:
-                if convert_dtype(dtype) != convert_dtype(data.dtype):
-                    return data.astype(convert_dtype(dtype))
-            return data
+            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 = paddle.Tensor(data)
+            return _handle_diff_place_dtype(data, dtype, place, 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".