add + - * / @ [] operator to ComplexVariable (#28217)

* add + - * / @ [] operator to ComplexVariable, also add unittest

* fix circular reference bug

* fit for py2.7

* remove reverse oprators which not supported now

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
+from ..framework import Variable, convert_np_dtype_to_dtype_, _varbase_creator, ComplexVariable
 from ..layers.layer_function_generator import OpProtoHolder
 from . import no_grad
 
@@ -149,6 +149,13 @@ def monkey_patch_math_varbase():
                          reverse=False,
                          scalar_method=None):
         def __impl__(self, other_var):
+            # 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)
+
             # FIXME(zjl): elementwise_div between integers cannot be converted to scale,
             # which may lose accuracy. This is a hot fix for release 1.6.
             if scalar_method is not None and not (

python/paddle/fluid/framework.py

@@ -1826,6 +1826,9 @@ class ComplexVariable(object):
             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

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

@@ -47,23 +47,36 @@ class TestComplexElementwiseLayers(unittest.TestCase):
             self.assertTrue(np.allclose(self.calc(x, y, "mul", place), x * y))
             self.assertTrue(np.allclose(self.calc(x, y, "div", place), x / y))
 
+    def compare_op(self, x, y):
+        for place in self._places:
+            with dg.guard(place):
+                var_x = dg.to_variable(x)
+                var_y = dg.to_variable(y)
+                self.assertTrue(var_x + var_y, x + y)
+                self.assertTrue(var_x - var_y, x - y)
+                self.assertTrue(var_x * var_y, x * y)
+                self.assertTrue(var_x / var_y, x / y)
+
     def test_complex_xy(self):
         x = rand([2, 3, 4, 5]).astype(self._dtype) + 1j * rand(
             [2, 3, 4, 5]).astype(self._dtype)
         y = rand([2, 3, 4, 5]).astype(self._dtype) + 1j * rand(
             [2, 3, 4, 5]).astype(self._dtype)
         self.compare(x, y)
+        self.compare_op(x, y)
 
     def test_complex_x_real_y(self):
         x = rand([2, 3, 4, 5]).astype(self._dtype) + 1j * rand(
             [2, 3, 4, 5]).astype(self._dtype)
         y = rand([4, 5]).astype(self._dtype)
         self.compare(x, y)
+        self.compare_op(x, y)
 
     def test_real_x_complex_y(self):
         x = rand([2, 3, 4, 5]).astype(self._dtype)
         y = rand([5]).astype(self._dtype) + 1j * rand([5]).astype(self._dtype)
         self.compare(x, y)
+        self.compare_op(x, y)
 
 
 if __name__ == '__main__':

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import paddle
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.dygraph as dg
+
+
+class TestComplexGetitemLayer(unittest.TestCase):
+    def setUp(self):
+        self._places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            self._places.append(fluid.CUDAPlace(0))
+
+    def test_case1(self):
+        x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
+        x_np_slice = x_np[0]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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):
+        x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
+        x_np_slice = x_np[0][1:3][0:4:2]
+
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x_np)
+                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__':
+    unittest.main()

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

@@ -34,6 +34,15 @@ class TestComplexMatMulLayer(unittest.TestCase):
         np_result = np.matmul(x, y)
         self.assertTrue(np.allclose(result.numpy(), np_result))
 
+    def compare_op(self, x, y):
+        for place in self._places:
+            with dg.guard(place):
+                x_var = dg.to_variable(x)
+                y_var = dg.to_variable(y)
+                result = x_var.matmul(y_var)
+        np_result = np.matmul(x, y)
+        self.assertTrue(np.allclose(result.numpy(), np_result))
+
     def test_complex_xy(self):
         x = np.random.random(
             (2, 3, 4, 5)).astype("float32") + 1J * np.random.random(
@@ -42,6 +51,7 @@ class TestComplexMatMulLayer(unittest.TestCase):
             (2, 3, 5, 4)).astype("float32") + 1J * np.random.random(
                 (2, 3, 5, 4)).astype("float32")
         self.compare(x, y)
+        self.compare_op(x, y)
 
     def test_complex_x(self):
         x = np.random.random(
@@ -49,6 +59,7 @@ class TestComplexMatMulLayer(unittest.TestCase):
                 (2, 3, 4, 5)).astype("float32")
         y = np.random.random((2, 3, 5, 4)).astype("float32")
         self.compare(x, y)
+        self.compare_op(x, y)
 
     def test_complex_y(self):
         x = np.random.random((2, 3, 4, 5)).astype("float32")

python/paddle/incubate/complex/__init__.py

@@ -13,6 +13,9 @@
 # limitations under the License.
 
 from . import tensor
+from .tensor_op_patch import monkey_patch_math_complex
 from .tensor import *
 
 __all__ = tensor.__all__ + []
+
+monkey_patch_math_complex()

python/paddle/incubate/complex/tensor_op_patch.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import division
+from ...fluid import framework
+from . import tensor
+
+
+def monkey_patch_math_complex():
+    # complexVariable do not support scaler type now, so here not contains
+    # reverse methods, such as "__radd__", "__rsub__", "__rmul__", "__rdiv__",
+    # "__rtruediv__", "__rmatmul__".
+    complex_methods = [
+        ('__add__', _binary_creator_('__add__', "elementwise_add", False)),
+        ('__sub__', _binary_creator_('__sub__', "elementwise_sub", False)),
+        ('__mul__', _binary_creator_('__mul__', "elementwise_mul", False)),
+        ('__div__', _binary_creator_('__div__', "elementwise_div", False)),
+        ('__truediv__', _binary_creator_('__truediv__', "elementwise_div",
+                                         False)),
+        ('__matmul__', _binary_creator_('__matmul__', "matmul", False)),
+    ]
+
+    for method in complex_methods:
+        method_name = method[0]
+        method_impl = method[1]
+        if method_impl:
+            setattr(framework.ComplexVariable, method_name, method_impl)
+
+    for method in tensor.__all__:
+        method_impl = getattr(tensor, method)
+        if method_impl:
+            setattr(framework.ComplexVariable, method, method_impl)
+
+
+# for binary operator such as elementwise
+def _binary_creator_(method_name, op_type, reverse=False):
+    def __impl__(self, other_var):
+        math_op = getattr(tensor, op_type)
+        return math_op(self, other_var)
+
+    __impl__.__name__ = method_name
+    return __impl__