set_default_type only take effect on python floats or complex (#26939)

* set_default_type only take effect on python floats or complex

* fix doc

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

@@ -33,16 +33,28 @@ class TestVarBase(unittest.TestCase):
         def _test_place(place):
             with fluid.dygraph.guard():
                 paddle.set_default_dtype('float32')
+                # set_default_dtype should not take effect on int
                 x = paddle.to_tensor(1, place=place, stop_gradient=False)
                 self.assertTrue(np.array_equal(x.numpy(), [1]))
                 self.assertNotEqual(x.dtype, core.VarDesc.VarType.FP32)
 
+                # set_default_dtype should not take effect on numpy
+                x = paddle.to_tensor(
+                    np.array([1.2]).astype('float16'),
+                    place=place,
+                    stop_gradient=False)
+                self.assertTrue(
+                    np.array_equal(x.numpy(), np.array([1.2], 'float16')))
+                self.assertEqual(x.dtype, core.VarDesc.VarType.FP16)
+
+                # set_default_dtype take effect on float
                 x = paddle.to_tensor(1.2, place=place, stop_gradient=False)
                 self.assertTrue(
                     np.array_equal(x.numpy(), np.array([1.2]).astype(
                         'float32')))
                 self.assertEqual(x.dtype, core.VarDesc.VarType.FP32)
 
+                # set_default_dtype take effect on complex
                 x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False)
                 self.assertTrue(np.array_equal(x.numpy(), [1 + 2j]))
                 self.assertEqual(x.dtype, 'complex64')

python/paddle/tensor/creation.py

@@ -73,8 +73,8 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor, paddle\.ComplexTensor.
         dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 
             'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8'. And
-            'complex64' , 'complex128' only for ComplexTensor. Default: None, for float point number, 
-            get type from ``get_default_type``, for other type, infers from ``data`` .
+            'complex64' , 'complex128' only for ComplexTensor. Default: None, infers dtype from ``data`` 
+            except for python float number which gets dtype from ``get_default_type`` .
         place(CPUPlace|CUDAPinnedPlace|CUDAPlace, optional): The place to allocate Tensor. Can be  
             CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place.
         stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
@@ -188,13 +188,21 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             raise TypeError(
                 "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor|paddle.ComplexTensor".
                 format(type(data)))
+        if not dtype and data.dtype in [
+                'float16', 'float32', 'float64', 'complex64', 'complex128'
+        ]:
+            default_type = paddle.get_default_dtype()
+            if np.iscomplexobj(data):
+                default_type = 'complex64' if default_type in [
+                    'float16', 'float32'
+                ] else 'complex128'
+            data = data.astype(default_type)
+
+    if dtype and convert_dtype(dtype) != data.dtype:
+        data = data.astype(dtype)
 
     if not np.iscomplexobj(data):
-        if dtype:
-            dtype = convert_dtype(dtype)
-        elif data.dtype in ['float16', 'float32', 'float64']:
-            dtype = paddle.framework.get_default_dtype()
-        if dtype and dtype != data.dtype:
+        if dtype and convert_dtype(dtype) != data.dtype:
             data = data.astype(dtype)
         return paddle.Tensor(
             value=data,
@@ -203,14 +211,6 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
             zero_copy=True,
             stop_gradient=stop_gradient)
     else:
-        if dtype:
-            dtype = convert_dtype(dtype)
-        else:
-            dtype = paddle.framework.get_default_dtype()
-            dtype = 'complex64' if dtype in ['float16', 'float32'
-                                             ] else 'complex128'
-        if dtype != data.dtype:
-            data = data.astype(dtype)
         name = unique_name.generate('generated_tensor')
         real_tensor = paddle.Tensor(
             value=data.real,