remove complexvariable (#29390)

* rm complexvariable

* modify test_var_base unittest

* remove duplicated codes

python/paddle/__init__.py

@@ -32,7 +32,6 @@ monkey_patch_variable()
 monkey_patch_math_varbase()
 import paddle.framework
 from .framework import VarBase as Tensor
-from .framework import ComplexVariable as ComplexTensor
 import paddle.compat
 import paddle.distributed
 import paddle.sysconfig
@@ -43,7 +42,6 @@ import paddle.distributed.fleet
 import paddle.optimizer
 import paddle.metric
 import paddle.device
-import paddle.incubate.complex as complex
 import paddle.regularizer
 
 # TODO: define alias in tensor and framework directory

python/paddle/fluid/dygraph/base.py

@@ -593,12 +593,12 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
     r"""
     :api_attr: imperative
 
-    The API will create a ``Variable`` or ``ComplexVariable`` object from 
-    tuple, list, numpy\.ndarray, Variable or ComplexVariable object.
+    The API will create a ``Variable`` object from 
+    tuple, list, numpy\.ndarray or Variable object.
 
     Parameters:
-        value(tuple|list|ndarray|Variable|Tensor|ComplexVariable): Initial data. 
-            Can be a list, tuple, NumPy ndarray, Variable, Tensor, ComplexVariable. 
+        value(tuple|list|ndarray|Variable|Tensor): Initial data. 
+            Can be a list, tuple, NumPy ndarray, Variable, Tensor.
             The shape can be multi-dimensional. The data type is one of 
             numpy\.{float16, float32, float64, int16, int32, int64, 
             uint8, uint16, complex64, complex128}.
@@ -613,10 +613,9 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
             'int32' , 'int64' , 'uint8' . Default: None.
 
     Returns:
-        Variable or ComplexVariable: If ``value`` is a tuple/list/numpy\.ndarray object, 
+        Variable : If ``value`` is a tuple/list/numpy\.ndarray object, 
             return ``Tensor`` created from the corresponding numpy\.ndarray object, which has 
-            same data type and shape with ``value``. If ``value`` is a Variable or ComplexVariable 
-            object, just return ``value``.
+            same data type and shape with ``value``. 
 
 
     Examples:
@@ -647,13 +646,12 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
 
     """
     support_type = (list, tuple, np.ndarray, core.VarBase, framework.Variable,
-                    framework.ComplexVariable, core.Tensor, core.LoDTensor)
+                    core.Tensor, core.LoDTensor)
     if not isinstance(value, support_type):
         raise TypeError(
             "The type of 'value' in fluid.dygraph.to_variable must be %s, but received %s."
             % (support_type, type(value)))
-    if isinstance(value, (core.VarBase, framework.Variable,
-                          framework.ComplexVariable)):
+    if isinstance(value, (core.VarBase, framework.Variable)):
         return value
     elif isinstance(value, (core.Tensor, core.LoDTensor)):
         return core.VarBase(value)
@@ -682,23 +680,6 @@ def to_variable(value, name=None, zero_copy=None, dtype=None):
             if value.dtype != dtype:
                 value = value.astype(dtype)
 
-        if np.iscomplexobj(value):
-            if not name:
-                name = framework.unique_name.generate('_generated_var')
-            real_var = core.VarBase(
-                value=value.real,
-                place=framework._current_expected_place(),
-                persistable=False,
-                zero_copy=zero_copy,
-                name=name + ".real")
-            imag_var = core.VarBase(
-                value=value.imag,
-                place=framework._current_expected_place(),
-                persistable=False,
-                zero_copy=zero_copy,
-                name=name + ".imag")
-            return framework.ComplexVariable(real_var, imag_var)
-        else:
         py_var = core.VarBase(
             value=value,
             place=framework._current_expected_place(),

python/paddle/fluid/dygraph/math_op_patch.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from .. import core
-from ..framework import Variable, convert_np_dtype_to_dtype_, _varbase_creator, ComplexVariable
+from ..framework import Variable, convert_np_dtype_to_dtype_, _varbase_creator
 from ..layers.layer_function_generator import OpProtoHolder
 from . import no_grad
 
@@ -170,13 +170,6 @@ def monkey_patch_math_varbase():
                          reverse=False,
                          scalar_method=None):
         def __impl__(self, other_var):
-            # 0. check tensor and ComplexVariable opetator
-            if isinstance(other_var, ComplexVariable):
-                # need import paddle in closure
-                import paddle
-                math_op = getattr(paddle.incubate.complex.tensor, op_type)
-                return math_op(self, other_var)
-
             # 1. scalar exists cases
             # we need combine the tensor.dtype and scalar.dtype, cast correct object
             if isinstance(other_var, float):

python/paddle/fluid/framework.py

@@ -51,7 +51,6 @@ __all__ = [
     'is_compiled_with_cuda',
     'is_compiled_with_xpu',
     'Variable',
-    'ComplexVariable',
     'load_op_library',
     'require_version',
     'device_guard',
@@ -1786,97 +1785,6 @@ def get_all_op_protos():
     return ret_values
 
 
-class ComplexVariable(object):
-    """
-    The ComplexTensor defined on the complex number domain. It contains two common 
-    real number Tensor as its members, :attr:`real` and :attr:`imag` 
-    holding the real part and imaginary part of complex numbers respectively.
-    
-    **Notes**:
-        **The constructor of ComplexTensor should not be invoked directly.**
-
-    Args:
-        real (Tensor): The Tensor holding real-part data.
-        imag (Tensor): The Tensor holding imaginery-part data.
-    
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            x = paddle.to_tensor([1.0+2.0j, 0.2])
-            print(x.name, x.dtype, x.shape)
-            # ({'real': 'generated_tensor_0.real', 'imag': 'generated_tensor_0.imag'}, complex64, [2])
-            print(x)
-            # ComplexTensor[real](shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-            #                     [        1., 0.20000000])
-            # ComplexTensor[imag](shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-            #                     [2., 0.])
-            print(type(x))
-            # <class 'paddle.ComplexTensor'>
-    """
-
-    def __new__(cls, *arg, **kwargs):
-        cls.__module__ = "paddle"
-        cls.__name__ = "ComplexTensor"
-        return super(ComplexVariable, cls).__new__(cls)
-
-    def __init__(self, real, imag):
-        assert real.shape == imag.shape, "The real part and imaginary part " \
-            "of a ComplexVariable should have the same shape!"
-        assert real.dtype == imag.dtype, "The real part and imaginary part " \
-            "of a ComplexVariable should have the same data type!"
-
-        self.real = real
-        self.imag = imag
-        if self.real.dtype in [
-                core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32
-        ]:
-            self._dtype = "complex64"
-        else:
-            self._dtype = "complex128"
-        self._shape = self.real.shape
-
-    def __getitem__(self, idx):
-        return ComplexVariable(self.real[idx], self.imag[idx])
-
-    @property
-    def dtype(self):
-        return self._dtype
-
-    @property
-    def shape(self):
-        return self._shape
-
-    @property
-    def name(self):
-        return {"real": self.real.name, "imag": self.imag.name}
-
-    @name.setter
-    def name(self, name):
-        # rename
-        if isinstance(name, str):
-            self.real.name = name + ".real"
-            self.imag.name = name + ".imag"
-        elif (isinstance(name, tuple) or isinstance(name,
-                                                    list)) and len(name) == 2:
-            self.real.name, self.imag.name = name[0], name[1]
-        else:
-            raise ValueError(
-                "An invalid name assigned to the ComplexVariable, "
-                "which must be a string, or a tuple or a list with length 2!")
-
-    def numpy(self):
-        return self.real.numpy() + 1j * self.imag.numpy()
-
-    def __str__(self):
-        from paddle.tensor.to_string import to_string
-        return "ComplexTensor containing:\n{real}\n{imag}".format(
-            real=to_string(self.real, "[real part]Tensor"),
-            imag=to_string(self.imag, "[imag part]Tensor"))
-
-    __repr__ = __str__
-
-
 class OpProtoHolder(object):
     """
     A global variable to hold all OpProtos from C++ as a map

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

@@ -19,14 +19,6 @@ from numpy.random import random as rand
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
-from paddle import complex as cpx
-
-layers = {
-    "add": cpx.elementwise_add,
-    "sub": cpx.elementwise_sub,
-    "mul": cpx.elementwise_mul,
-    "div": cpx.elementwise_div,
-}
 
 paddle_apis = {
     "add": paddle.add,
@@ -43,26 +35,10 @@ class TestComplexElementwiseLayers(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self._places.append(paddle.CUDAPlace(0))
 
-    def calc(self, x, y, op, place):
-        with dg.guard(place):
-            var_x = dg.to_variable(x)
-            var_y = dg.to_variable(y)
-            return layers[op](var_x, var_y).numpy()
-
     def paddle_calc(self, x, y, op, place):
         with dg.guard(place):
-            x_t = paddle.Tensor(
-                value=x,
-                place=place,
-                persistable=False,
-                zero_copy=False,
-                stop_gradient=True)
-            y_t = paddle.Tensor(
-                value=y,
-                place=place,
-                persistable=False,
-                zero_copy=False,
-                stop_gradient=True)
+            x_t = dg.to_variable(x)
+            y_t = dg.to_variable(y)
             return paddle_apis[op](x_t, y_t).numpy()
 
     def assert_check(self, pd_result, np_result, place):
@@ -72,13 +48,6 @@ class TestComplexElementwiseLayers(unittest.TestCase):
             format(place, pd_result[~np.isclose(pd_result, np_result)],
                    np_result[~np.isclose(pd_result, np_result)]))
 
-    def compare_by_complex_api(self, x, y):
-        for place in self._places:
-            self.assert_check(self.calc(x, y, "add", place), x + y, place)
-            self.assert_check(self.calc(x, y, "sub", place), x - y, place)
-            self.assert_check(self.calc(x, y, "mul", place), x * y, place)
-            self.assert_check(self.calc(x, y, "div", place), x / y, place)
-
     def compare_by_basic_api(self, x, y):
         for place in self._places:
             self.assert_check(
@@ -90,7 +59,7 @@ class TestComplexElementwiseLayers(unittest.TestCase):
             self.assert_check(
                 self.paddle_calc(x, y, "div", place), x / y, place)
 
-    def compare_op_by_complex_api(self, x, y):
+    def compare_op_by_basic_api(self, x, y):
         for place in self._places:
             with dg.guard(place):
                 var_x = dg.to_variable(x)
@@ -100,26 +69,6 @@ class TestComplexElementwiseLayers(unittest.TestCase):
                 self.assert_check((var_x * var_y).numpy(), x * y, place)
                 self.assert_check((var_x / var_y).numpy(), x / y, place)
 
-    def compare_op_by_basic_api(self, x, y):
-        for place in self._places:
-            with dg.guard(place):
-                x_t = paddle.Tensor(
-                    value=x,
-                    place=place,
-                    persistable=False,
-                    zero_copy=False,
-                    stop_gradient=True)
-                y_t = paddle.Tensor(
-                    value=y,
-                    place=place,
-                    persistable=False,
-                    zero_copy=False,
-                    stop_gradient=True)
-                self.assert_check((x_t + y_t).numpy(), x + y, place)
-                self.assert_check((x_t - y_t).numpy(), x - y, place)
-                self.assert_check((x_t * y_t).numpy(), x * y, place)
-                self.assert_check((x_t / y_t).numpy(), x / y, place)
-
     def test_complex_xy(self):
         for dtype in self._dtypes:
             x = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
@@ -127,10 +76,7 @@ class TestComplexElementwiseLayers(unittest.TestCase):
             y = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
                 [2, 3, 4, 5]).astype(dtype)
 
-            self.compare_by_complex_api(x, y)
-            self.compare_op_by_complex_api(x, y)
-
-            self.compare_op_by_complex_api(x, y)
+            self.compare_by_basic_api(x, y)
             self.compare_op_by_basic_api(x, y)
 
     def test_complex_x_real_y(self):
@@ -139,9 +85,6 @@ class TestComplexElementwiseLayers(unittest.TestCase):
                 [2, 3, 4, 5]).astype(dtype)
             y = rand([4, 5]).astype(dtype)
 
-            self.compare_by_complex_api(x, y)
-            self.compare_op_by_complex_api(x, y)
-
             # promote types cases
             self.compare_by_basic_api(x, y)
             self.compare_op_by_basic_api(x, y)
@@ -151,9 +94,6 @@ class TestComplexElementwiseLayers(unittest.TestCase):
             x = rand([2, 3, 4, 5]).astype(dtype)
             y = rand([5]).astype(dtype) + 1j * rand([5]).astype(dtype)
 
-            self.compare_by_complex_api(x, y)
-            self.compare_op_by_complex_api(x, y)
-
             # promote types cases
             self.compare_by_basic_api(x, y)
             self.compare_op_by_basic_api(x, y)

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

@@ -36,18 +36,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0]
-
-            np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
-
     def test_case2(self):
         x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
         x_np_slice = x_np[0][1]
@@ -59,18 +47,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0][1]
-
-            np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
-
     def test_case3(self):
         x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
         x_np_slice = x_np[0][1][2]
@@ -82,18 +58,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0][1][2]
-
-            np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
-
     def test_case4(self):
         x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
         x_np_slice = x_np[0][1][0:3]
@@ -105,18 +69,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0][1][0:3]
-
-            np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
-
     def test_case5(self):
         x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
         x_np_slice = x_np[0][1][0:4:2]
@@ -128,16 +80,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0][1][0:4:2]
-
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
     def test_case6(self):
@@ -150,17 +92,6 @@ class TestComplexGetitemLayer(unittest.TestCase):
                 x_var_slice = x_var[0][1:3][0:4:2]
 
             np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x_np,
-                    place=fluid.framework._current_expected_place(),
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                x_var_slice = x_var[0][1:3][0:4:2]
-
-            np.testing.assert_allclose(x_var_slice.numpy(), x_np_slice)
 
 
 if __name__ == '__main__':

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

@@ -33,33 +33,13 @@ class ComplexKronTestCase(unittest.TestCase):
 
     def runTest(self):
         for place in self._places:
-            self.test_complex_api(place)
-            self.test_basic_api(place)
+            self.test_kron_api(place)
 
-    def test_complex_api(self, place):
+    def test_kron_api(self, place):
         with dg.guard(place):
             x_var = dg.to_variable(self.x)
             y_var = dg.to_variable(self.y)
-            out_var = paddle.complex.kron(x_var, y_var)
-            self.assertTrue(np.allclose(out_var.numpy(), self.ref_result))
-
-    def test_basic_api(self, place):
-        with dg.guard(place):
-            x_var = paddle.Tensor(
-                value=self.x,
-                place=place,
-                persistable=False,
-                zero_copy=None,
-                stop_gradient=True)
-
-            y_var = paddle.Tensor(
-                value=self.y,
-                place=place,
-                persistable=False,
-                zero_copy=None,
-                stop_gradient=True)
-
-            out_var = tensor.math.kron(x_var, y_var)
+            out_var = paddle.kron(x_var, y_var)
             self.assertTrue(np.allclose(out_var.numpy(), self.ref_result))
 
 

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

@@ -26,34 +26,11 @@ class TestComplexMatMulLayer(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self._places.append(fluid.CUDAPlace(0))
 
-    def compare_by_complex_api(self, x, y, np_result):
+    def compare_by_basic_api(self, x, y, np_result):
         for place in self._places:
             with dg.guard(place):
                 x_var = dg.to_variable(x)
                 y_var = dg.to_variable(y)
-                result = paddle.complex.matmul(x_var, y_var)
-                pd_result = result.numpy()
-                self.assertTrue(
-                    np.allclose(pd_result, np_result),
-                    "\nplace: {}\npaddle diff result:\n {}\nnumpy diff result:\n {}\n".
-                    format(place, pd_result[~np.isclose(pd_result, np_result)],
-                           np_result[~np.isclose(pd_result, np_result)]))
-
-    def compare_by_basic_api(self, x, y, np_result):
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x,
-                    place=place,
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                y_var = fluid.core.VarBase(
-                    value=y,
-                    place=place,
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
                 result = paddle.matmul(x_var, y_var)
                 pd_result = result.numpy()
                 self.assertTrue(
@@ -62,7 +39,7 @@ class TestComplexMatMulLayer(unittest.TestCase):
                     format(place, pd_result[~np.isclose(pd_result, np_result)],
                            np_result[~np.isclose(pd_result, np_result)]))
 
-    def compare_op_by_complex_api(self, x, y, np_result):
+    def compare_op_by_basic_api(self, x, y, np_result):
         for place in self._places:
             with dg.guard(place):
                 x_var = dg.to_variable(x)
@@ -75,29 +52,6 @@ class TestComplexMatMulLayer(unittest.TestCase):
                     format(place, pd_result[~np.isclose(pd_result, np_result)],
                            np_result[~np.isclose(pd_result, np_result)]))
 
-    def compare_op_by_basic_api(self, x, y, np_result):
-        for place in self._places:
-            with dg.guard(place):
-                x_var = fluid.core.VarBase(
-                    value=x,
-                    place=place,
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                y_var = fluid.core.VarBase(
-                    value=y,
-                    place=place,
-                    persistable=False,
-                    zero_copy=None,
-                    name='')
-                result = x_var.matmul(y_var)
-                pd_result = result.numpy()
-                self.assertTrue(
-                    np.allclose(pd_result, np_result),
-                    "\nplace: {}\npaddle diff result:\n {}\nnumpy diff result:\n {}\n".
-                    format(place, pd_result[~np.isclose(pd_result, np_result)],
-                           np_result[~np.isclose(pd_result, np_result)]))
-
     def test_complex_xy(self):
         for dtype in self._dtypes:
             x = np.random.random(
@@ -109,9 +63,6 @@ class TestComplexMatMulLayer(unittest.TestCase):
 
             np_result = np.matmul(x, y)
 
-            self.compare_by_complex_api(x, y, np_result)
-            self.compare_op_by_complex_api(x, y, np_result)
-
             self.compare_by_basic_api(x, y, np_result)
             self.compare_op_by_basic_api(x, y, np_result)
 
@@ -124,9 +75,6 @@ class TestComplexMatMulLayer(unittest.TestCase):
 
             np_result = np.matmul(x, y)
 
-            self.compare_by_complex_api(x, y, np_result)
-            self.compare_op_by_complex_api(x, y, np_result)
-
             # float -> complex type promotion
             self.compare_by_basic_api(x, y, np_result)
             self.compare_op_by_basic_api(x, y, np_result)
@@ -140,8 +88,6 @@ class TestComplexMatMulLayer(unittest.TestCase):
 
             np_result = np.matmul(x, y)
 
-            self.compare_by_complex_api(x, y, np_result)
-
             # float -> complex type promotion
             self.compare_by_basic_api(x, y, np_result)
             self.compare_op_by_basic_api(x, y, np_result)

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

@@ -14,7 +14,6 @@
 
 import paddle.fluid as fluid
 import paddle
-from paddle import complex as cpx
 import paddle.fluid.dygraph as dg
 import numpy as np
 import unittest
@@ -27,7 +26,7 @@ class TestComplexReshape(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self._places.append(paddle.CUDAPlace(0))
 
-    def test_case1(self):
+    def test_shape_norm_dims(self):
         for dtype in self._dtypes:
             x_np = np.random.randn(
                 2, 3, 4).astype(dtype) + 1j * np.random.randn(2, 3,
@@ -36,11 +35,11 @@ class TestComplexReshape(unittest.TestCase):
             for place in self._places:
                 with dg.guard(place):
                     x_var = dg.to_variable(x_np)
-                    y_var = cpx.reshape(x_var, shape)
+                    y_var = paddle.reshape(x_var, shape)
                     y_np = y_var.numpy()
-                    np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
+                    self.assertTrue(np.allclose(np.reshape(x_np, shape), y_np))
 
-    def test_case2(self):
+    def test_shape_omit_dims(self):
         for dtype in self._dtypes:
             x_np = np.random.randn(
                 2, 3, 4).astype(dtype) + 1j * np.random.randn(2, 3,
@@ -50,43 +49,9 @@ class TestComplexReshape(unittest.TestCase):
             for place in self._places:
                 with dg.guard(place):
                     x_var = dg.to_variable(x_np)
-                    y_var = cpx.reshape(x_var, shape, inplace=True)
+                    y_var = paddle.reshape(x_var, shape)
                     y_np = y_var.numpy()
-                    np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
-
-    def test_case3(self):
-        for dtype in self._dtypes:
-            x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
-            shape = (2, -1)
-            for place in self._places:
-                with dg.guard(place):
-                    x_var = paddle.Tensor(
-                        value=x_np,
-                        place=fluid.framework._current_expected_place(),
-                        persistable=False,
-                        zero_copy=None,
-                        stop_gradient=True)
-                    y_var = fluid.layers.reshape(x_var, shape)
-                    y_np = y_var.numpy()
-                    np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
-
-    def test_case4(self):
-        for dtype in self._dtypes:
-            x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
-            shape = (0, -1)
-            shape_ = (2, 12)
-
-            for place in self._places:
-                with dg.guard(place):
-                    x_var = paddle.Tensor(
-                        value=x_np,
-                        place=fluid.framework._current_expected_place(),
-                        persistable=False,
-                        zero_copy=None,
-                        stop_gradient=True)
-                    y_var = fluid.layers.reshape(x_var, shape)
-                    y_np = y_var.numpy()
-                    np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
+                    self.assertTrue(np.allclose(np.reshape(x_np, shape_), y_np))
 
 
 if __name__ == "__main__":

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

@@ -16,7 +16,6 @@ import unittest
 import numpy as np
 import paddle
 from numpy.random import random as rand
-from paddle import complex as cpx
 from paddle import tensor
 import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
@@ -29,29 +28,13 @@ class TestComplexSumLayer(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self._places.append(paddle.CUDAPlace(0))
 
-    def test_complex_x(self):
-        for dtype in self._dtypes:
-            input = rand([2, 10, 10]).astype(dtype) + 1j * rand(
-                [2, 10, 10]).astype(dtype)
-            for place in self._places:
-                with dg.guard(place):
-                    var_x = dg.to_variable(input)
-                    result = cpx.sum(var_x, dim=[1, 2]).numpy()
-                    target = np.sum(input, axis=(1, 2))
-                    self.assertTrue(np.allclose(result, target))
-
     def test_complex_basic_api(self):
         for dtype in self._dtypes:
             input = rand([2, 10, 10]).astype(dtype) + 1j * rand(
                 [2, 10, 10]).astype(dtype)
             for place in self._places:
                 with dg.guard(place):
-                    var_x = paddle.Tensor(
-                        value=input,
-                        place=place,
-                        persistable=False,
-                        zero_copy=None,
-                        stop_gradient=True)
+                    var_x = dg.to_variable(input)
                     result = tensor.sum(var_x, axis=[1, 2]).numpy()
                     target = np.sum(input, axis=(1, 2))
                     self.assertTrue(np.allclose(result, target))

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

@@ -16,7 +16,6 @@ import unittest
 import numpy as np
 import paddle
 from numpy.random import random as rand
-from paddle import complex as cpx
 from paddle import tensor
 import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
@@ -29,30 +28,13 @@ class TestComplexTraceLayer(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self._places.append(fluid.CUDAPlace(0))
 
-    def test_complex_api(self):
-        for dtype in self._dtypes:
-            input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
-                [2, 20, 2, 3]).astype(dtype)
-            for place in self._places:
-                with dg.guard(place):
-                    var_x = dg.to_variable(input)
-                    result = cpx.trace(
-                        var_x, offset=1, axis1=0, axis2=2).numpy()
-                    target = np.trace(input, offset=1, axis1=0, axis2=2)
-                    self.assertTrue(np.allclose(result, target))
-
     def test_basic_api(self):
         for dtype in self._dtypes:
             input = rand([2, 20, 2, 3]).astype(dtype) + 1j * rand(
                 [2, 20, 2, 3]).astype(dtype)
             for place in self._places:
                 with dg.guard(place):
-                    var_x = paddle.Tensor(
-                        value=input,
-                        place=place,
-                        persistable=False,
-                        zero_copy=None,
-                        stop_gradient=True)
+                    var_x = dg.to_variable(input)
                     result = tensor.trace(
                         var_x, offset=1, axis1=0, axis2=2).numpy()
                     target = np.trace(input, offset=1, axis1=0, axis2=2)

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

@@ -36,24 +36,6 @@ class TestComplexTransposeLayer(unittest.TestCase):
             for place in self._places:
                 with dg.guard(place):
                     var = dg.to_variable(data)
-                    trans = paddle.complex.transpose(var, perm=perm)
-                self.assertTrue(np.allclose(trans.numpy(), np_trans))
-
-    def test_transpose_by_basic_api(self):
-        for dtype in self._dtypes:
-            data = np.random.random(
-                (2, 3, 4, 5)).astype(dtype) + 1J * np.random.random(
-                    (2, 3, 4, 5)).astype(dtype)
-            perm = [3, 2, 0, 1]
-            np_trans = np.transpose(data, perm)
-            for place in self._places:
-                with dg.guard(place):
-                    var = paddle.Tensor(
-                        value=data,
-                        place=place,
-                        persistable=False,
-                        zero_copy=None,
-                        stop_gradient=True)
                     trans = paddle.transpose(var, perm=perm)
                 self.assertTrue(np.allclose(trans.numpy(), np_trans))
 

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

@@ -30,14 +30,11 @@ class TestComplexVariable(unittest.TestCase):
         with dg.guard():
             x = dg.to_variable(a, "x")
             y = dg.to_variable(b)
-            out = paddle.complex.elementwise_add(x, y)
+            out = paddle.fluid.layers.elementwise_add(x, y)
             self.assertIsNotNone("{}".format(out))
 
         self.assertTrue(np.allclose(out.numpy(), a + b))
-        self.assertEqual(x.name, {'real': 'x.real', 'imag': 'x.imag'})
-        x.name = "new_x"
-        self.assertEqual(x.name, {'real': 'new_x.real', 'imag': 'new_x.imag'})
-        self.assertEqual(out.dtype, self._dtype)
+        self.assertEqual(out.dtype, convert_np_dtype_to_dtype_(self._dtype))
         self.assertEqual(out.shape, x.shape)
 
     def test_attrs(self):

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

@@ -28,15 +28,6 @@ class TestIsTensorApi(unittest.TestCase):
         x = paddle.rand([3, 2, 4], dtype=dtype)
         self.assertTrue(paddle.is_tensor(x))
 
-    def test_is_tensor_complex(self, dtype="float32"):
-        """Test is_tensor api with a complex tensor
-        """
-        paddle.disable_static()
-        r = paddle.to_tensor(1)
-        i = paddle.to_tensor(2)
-        x = paddle.ComplexTensor(r, i)
-        self.assertTrue(paddle.is_tensor(x))
-
     def test_is_tensor_list(self, dtype="float32"):
         """Test is_tensor api with a list
         """

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

@@ -78,7 +78,7 @@ class TestVarBase(unittest.TestCase):
                 # 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')
+                self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX64)
 
                 paddle.set_default_dtype('float64')
                 x = paddle.to_tensor(1.2, place=place, stop_gradient=False)
@@ -87,7 +87,7 @@ class TestVarBase(unittest.TestCase):
 
                 x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False)
                 self.assertTrue(np.array_equal(x.numpy(), [1 + 2j]))
-                self.assertEqual(x.dtype, 'complex128')
+                self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX128)
 
                 x = paddle.to_tensor(
                     1, dtype='float32', place=place, stop_gradient=False)
@@ -133,10 +133,8 @@ class TestVarBase(unittest.TestCase):
                     [1 + 2j, 1 - 2j], dtype='complex64', place=place)
                 y = paddle.to_tensor(x)
                 self.assertTrue(np.array_equal(x.numpy(), [1 + 2j, 1 - 2j]))
-                self.assertEqual(y.dtype, 'complex64')
+                self.assertEqual(y.dtype, core.VarDesc.VarType.COMPLEX64)
                 self.assertEqual(y.shape, [2])
-                self.assertEqual(y.real.stop_gradient, True)
-                self.assertEqual(y.real.type, core.VarDesc.VarType.LOD_TENSOR)
 
                 with self.assertRaises(TypeError):
                     paddle.to_tensor('test')

python/paddle/framework/__init__.py

@@ -25,7 +25,6 @@ from .random import seed
 from .framework import get_default_dtype
 from .framework import set_default_dtype
 
-from ..fluid.framework import ComplexVariable  #DEFINE_ALIAS
 from ..fluid.param_attr import ParamAttr  #DEFINE_ALIAS
 # from ..fluid.layers.tensor import create_global_var  #DEFINE_ALIAS
 from ..fluid.layers.tensor import create_parameter  #DEFINE_ALIAS

python/paddle/tensor/creation.py

@@ -55,32 +55,29 @@ __all__ = [
 @dygraph_only
 def to_tensor(data, dtype=None, place=None, stop_gradient=True):
     r"""
-    Constructs a ``paddle.Tensor`` or ``paddle.ComplexTensor`` from ``data`` , 
-    which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor, paddle\.ComplexTensor.
+    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. 
 
-    The ``ComplexTensor`` is a unique type of paddle. If x is ``ComplexTensor``, then 
-    ``x.real`` is the real part, and ``x.imag`` is the imaginary part.
-
     Args:
-        data(scalar|tuple|list|ndarray|Tensor|ComplexTensor): Initial data for the tensor.
-            Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor, paddle\.ComplexTensor.
+        data(scalar|tuple|list|ndarray|Tensor): Initial data for the tensor.
+            Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
         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, infers dtype from ``data`` 
+            'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
+            'complex64' , 'complex128'. 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.
 
     Returns:
-        Tensor: A Tensor or ComplexTensor constructed from ``data`` .
+        Tensor: A Tensor constructed from ``data`` .
 
     Raises:
-        TypeError: If the data type of ``data`` is not scalar, list, tuple, numpy.ndarray, paddle.Tensor, paddle.ComplexTensor
+        TypeError: If the data type of ``data`` is not scalar, list, tuple, numpy.ndarray, paddle.Tensor
         ValueError: If ``data`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]
         TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
         ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace
@@ -112,16 +109,13 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
         #        [[0.10000000, 0.20000000],
         #         [0.30000001, 0.40000001]])
 
-        type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]]), dtype='complex64')
-        # <class 'paddle.ComplexTensor'>
+        type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
+        # <class 'paddle.VarBase'>
 
         paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
-        # ComplexTensor[real](shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-        #                     [[1., 2.],
-        #                      [3., 4.]])
-        # ComplexTensor[imag](shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
-        #                     [[1., 0.],
-        #                      [2., 0.]])
+        # Tensor(shape=[2, 2], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
+        #        [[(1+1j), (2+0j)],
+        #         [(3+2j), (4+0j)]])
     """
 
     if place is None:
@@ -156,11 +150,9 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
                 if convert_dtype(dtype) != convert_dtype(data.dtype):
                     return data.astype(convert_dtype(dtype))
             return data
-        elif isinstance(data, paddle.ComplexTensor):
-            return data
         else:
             raise TypeError(
-                "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor|paddle.ComplexTensor".
+                "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|numpy.ndarray|paddle.Tensor".
                 format(type(data)))
         if not dtype and data.dtype in [
                 'float16', 'float32', 'float64', 'complex64', 'complex128'
@@ -175,30 +167,12 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
     if dtype and convert_dtype(dtype) != data.dtype:
         data = data.astype(dtype)
 
-    if not np.iscomplexobj(data):
-        if dtype and convert_dtype(dtype) != data.dtype:
-            data = data.astype(dtype)
     return paddle.Tensor(
         value=data,
         place=place,
         persistable=False,
         zero_copy=False,
         stop_gradient=stop_gradient)
-    else:
-        name = unique_name.generate('generated_tensor')
-        real_tensor = paddle.Tensor(
-            value=data.real,
-            place=place,
-            zero_copy=False,
-            name=name + ".real",
-            stop_gradient=stop_gradient)
-        imag_tensor = paddle.Tensor(
-            value=data.imag,
-            place=place,
-            zero_copy=False,
-            name=name + ".imag",
-            stop_gradient=stop_gradient)
-        return paddle.ComplexTensor(real_tensor, imag_tensor)
 
 
 def full_like(x, fill_value, dtype=None, name=None):

python/paddle/tensor/logic.py

@@ -20,7 +20,6 @@ from .. import fluid
 from ..fluid.framework import in_dygraph_mode
 from paddle.common_ops_import import *
 from ..framework import VarBase as Tensor
-from ..framework import ComplexVariable as ComplexTensor
 
 # TODO: define logic functions of a tensor  
 from ..fluid.layers import is_empty  #DEFINE_ALIAS
@@ -445,13 +444,13 @@ def not_equal(x, y, name=None):
 def is_tensor(x):
     """
 
-    This function tests whether input object is a paddle.Tensor or a paddle.ComplexTensor.
+    This function tests whether input object is a paddle.Tensor.
 
     Args:
         x (object): Object to test.
 
     Returns:
-        A boolean value. True if 'x' is a paddle.Tensor or a paddle.ComplexTensor, otherwise False.
+        A boolean value. True if 'x' is a paddle.Tensor, otherwise False.
 
     Examples:
         .. code-block:: python
@@ -462,13 +461,9 @@ def is_tensor(x):
             check = paddle.is_tensor(input1)
             print(check)  #True
 
-            input2 = paddle.ComplexTensor(input1, input1)
-            check = paddle.is_tensor(input2)
-            print(check)  #True
-
             input3 = [1, 4]
             check = paddle.is_tensor(input3)
             print(check)  #False
             
     """
-    return isinstance(x, Tensor) or isinstance(x, ComplexTensor)
+    return isinstance(x, Tensor)

python/setup.py.in

@@ -143,8 +143,6 @@ packages=['paddle',
           'paddle.reader',
           'paddle.distributed',
           'paddle.incubate',
-          'paddle.incubate.complex',
-          'paddle.incubate.complex.tensor',
           'paddle.distributed.fleet',
           'paddle.distributed.fleet.base',
           'paddle.distributed.fleet.meta_optimizers',