chore: import upstream snapshot with attribution

test/legacy_test/test_normalization_wrapper.py

@@ -0,0 +1,98 @@
+#   Copyright (c) 2018 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 op_test import get_places
+
+import paddle
+from paddle import base
+
+
+class TestNormalization(unittest.TestCase):
+    data_desc = {"name": "input", "shape": (2, 3, 7)}
+
+    def gen_random_input(self):
+        """Generate random input data."""
+        self.data = np.random.random(size=self.data_desc["shape"]).astype(
+            "float32"
+        )
+
+    def set_program(self, axis, epsilon):
+        """Build the test program."""
+        data = paddle.static.data(
+            name=self.data_desc["name"],
+            shape=self.data_desc["shape"],
+            dtype="float32",
+        )
+        data.stop_gradient = False
+        l2_norm = paddle.nn.functional.normalize(
+            data, axis=axis, epsilon=epsilon
+        )
+        out = paddle.sum(l2_norm, axis=None)
+
+        base.backward.append_backward(loss=out)
+        self.fetch_list = [l2_norm]
+
+    def run_program(self):
+        """Run the test program."""
+        for place in get_places():
+            self.set_inputs(place)
+            exe = base.Executor(place)
+
+            (output,) = exe.run(
+                base.default_main_program(),
+                feed=self.inputs,
+                fetch_list=self.fetch_list,
+                return_numpy=True,
+            )
+            self.op_output = output
+
+    def set_inputs(self, place):
+        """Set the randomly generated data to the test program."""
+        self.inputs = {}
+        tensor = base.Tensor()
+        tensor.set(self.data, place)
+        self.inputs[self.data_desc["name"]] = tensor
+
+    def l2_normalize(self, data, axis, epsilon):
+        """Compute the groundtruth."""
+        output = data / np.broadcast_to(
+            np.sqrt(np.sum(np.square(data), axis=axis, keepdims=True)),
+            data.shape,
+        )
+        return output
+
+    def test_l2_normalize(self):
+        """Test the python wrapper for l2_normalize."""
+        axis = 1
+        # TODO(caoying) epsilon is not supported due to lack of a maximum_op.
+        epsilon = 1e-6
+
+        self.gen_random_input()
+
+        self.set_program(axis, epsilon)
+        self.run_program()
+
+        expect_output = self.l2_normalize(self.data, axis, epsilon)
+
+        # check output
+        np.testing.assert_allclose(
+            self.op_output, expect_output, rtol=1e-05, atol=0.001
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()