chore: import upstream snapshot with attribution

test/legacy_test/test_egr_code_generate_api.py

@@ -0,0 +1,67 @@
+# Copyright (c) 2021 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
+
+import paddle
+
+
+class EagerOpAPIGenerateTestCase(unittest.TestCase):
+    def test_elementwise_add(self):
+        paddle.set_device("cpu")
+        np_x = np.ones([4, 16, 16, 32]).astype('float32')
+        np_y = np.ones([4, 16, 16, 32]).astype('float32')
+        x = paddle.to_tensor(np_x)
+        y = paddle.to_tensor(np_y)
+        out = paddle.add(x, y)
+        out_arr = out.numpy()
+
+        out_arr_expected = np.add(np_x, np_y)
+        np.testing.assert_array_equal(out_arr, out_arr_expected)
+
+    def test_sum(self):
+        x_data = np.array([[0.2, 0.3, 0.5, 0.9], [0.1, 0.2, 0.6, 0.7]]).astype(
+            'float32'
+        )
+        x = paddle.to_tensor(x_data, 'float32')
+        out = paddle.sum(x, axis=0)
+        out_arr = out.numpy()
+        out_arr_expected = np.sum(x_data, axis=0)
+        np.testing.assert_array_equal(out_arr, out_arr_expected)
+
+    def test_mm(self):
+        np_input = np.random.random([16, 32]).astype('float32')
+        np_mat2 = np.random.random([32, 32]).astype('float32')
+        input = paddle.to_tensor(np_input)
+        mat2 = paddle.to_tensor(np_mat2)
+        out = paddle.mm(input, mat2)
+        out_arr = out.numpy()
+        out_arr_expected = np.matmul(np_input, np_mat2)
+        np.testing.assert_allclose(out_arr, out_arr_expected, rtol=1e-05)
+
+    def test_sigmoid(self):
+        np_x = np.array([-0.4, -0.2, 0.1, 0.3]).astype('float32')
+        x = paddle.to_tensor(np_x)
+        out = paddle.nn.functional.sigmoid(x)
+        out_arr = out.numpy()
+        out_arr_expected = np.array(
+            [0.40131234, 0.450166, 0.52497919, 0.57444252]
+        ).astype('float32')
+        np.testing.assert_allclose(out_arr, out_arr_expected, rtol=1e-05)
+
+
+if __name__ == "__main__":
+    unittest.main()