chore: import upstream snapshot with attribution

test/legacy_test/test_complex_elementwise_layers.py

@@ -0,0 +1,110 @@
+#   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 numpy as np
+from numpy.random import random as rand
+from op_test import get_places
+
+import paddle
+import paddle.base.dygraph as dg
+
+paddle_apis = {
+    "add": paddle.add,
+    "sub": paddle.subtract,
+    "mul": paddle.multiply,
+    "div": paddle.divide,
+}
+
+
+class TestComplexElementwiseLayers(unittest.TestCase):
+    def setUp(self):
+        self._dtypes = ["float32", "float64"]
+        self._places = get_places()
+
+    def paddle_calc(self, x, y, op, place):
+        with dg.guard(place):
+            x_t = paddle.to_tensor(x)
+            y_t = paddle.to_tensor(y)
+            return paddle_apis[op](x_t, y_t).numpy()
+
+    def assert_check(self, pd_result, np_result, place):
+        np.testing.assert_allclose(
+            pd_result,
+            np_result,
+            rtol=1e-05,
+            err_msg=f'\nplace: {place}\npaddle diff result:\n {pd_result[~np.isclose(pd_result, np_result)]}\nnumpy diff result:\n {np_result[~np.isclose(pd_result, np_result)]}\n',
+        )
+
+    def compare_by_basic_api(self, x, y):
+        for place in self._places:
+            self.assert_check(
+                self.paddle_calc(x, y, "add", place), x + y, place
+            )
+            self.assert_check(
+                self.paddle_calc(x, y, "sub", place), x - y, place
+            )
+            self.assert_check(
+                self.paddle_calc(x, y, "mul", place), x * y, place
+            )
+            self.assert_check(
+                self.paddle_calc(x, y, "div", place), x / y, place
+            )
+
+    def compare_op_by_basic_api(self, x, y):
+        for place in self._places:
+            with dg.guard(place):
+                var_x = paddle.to_tensor(x)
+                var_y = paddle.to_tensor(y)
+                self.assert_check((var_x + var_y).numpy(), x + y, place)
+                self.assert_check((var_x - var_y).numpy(), x - y, place)
+                self.assert_check((var_x * var_y).numpy(), x * y, place)
+                self.assert_check((var_x / var_y).numpy(), x / y, place)
+
+    def test_complex_xy(self):
+        for dtype in self._dtypes:
+            x = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
+                [2, 3, 4, 5]
+            ).astype(dtype)
+            y = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
+                [2, 3, 4, 5]
+            ).astype(dtype)
+
+            self.compare_by_basic_api(x, y)
+            self.compare_op_by_basic_api(x, y)
+
+    def test_complex_x_real_y(self):
+        for dtype in self._dtypes:
+            x = rand([2, 3, 4, 5]).astype(dtype) + 1j * rand(
+                [2, 3, 4, 5]
+            ).astype(dtype)
+            y = rand([4, 5]).astype(dtype)
+
+            # promote types cases
+            self.compare_by_basic_api(x, y)
+            self.compare_op_by_basic_api(x, y)
+
+    def test_real_x_complex_y(self):
+        for dtype in self._dtypes:
+            x = rand([2, 3, 4, 5]).astype(dtype)
+            y = rand([5]).astype(dtype) + 1j * rand([5]).astype(dtype)
+
+            # promote types cases
+            self.compare_by_basic_api(x, y)
+            self.compare_op_by_basic_api(x, y)
+
+
+if __name__ == '__main__':
+    unittest.main()