chore: import upstream snapshot with attribution

test/legacy_test/test_raw_program_optimizer.py

@@ -0,0 +1,81 @@
+#   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 os
+import unittest
+
+import numpy as np
+from op_test import get_device_place
+
+import paddle
+from paddle import base
+from paddle.distributed import fleet
+
+
+class TestRawProgramOptimizer(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+
+    def mlp(self, input_x, input_y, hid_dim=128, label_dim=2):
+        fc_1 = paddle.static.nn.fc(x=input_x, size=hid_dim, activation='tanh')
+        fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim, activation='tanh')
+        prediction = paddle.static.nn.fc(
+            x=[fc_2], size=label_dim, activation='softmax'
+        )
+        cost = paddle.nn.functional.cross_entropy(
+            input=prediction, label=input_y
+        )
+        avg_cost = paddle.mean(x=cost)
+        return avg_cost
+
+    def gen_data(self):
+        return {
+            "x": np.random.random(size=(128, 32)).astype('float32'),
+            "y": np.random.randint(2, size=(128, 1)).astype('int64'),
+        }
+
+    def test_single_gpu(self):
+        paddle.enable_static()
+        with paddle.pir_utils.OldIrGuard():
+            fleet.init(is_collective=True)
+            sharding_program = paddle.static.Program()
+            sharding_startup_program = paddle.static.Program()
+            strategy = fleet.DistributedStrategy()
+            strategy.without_graph_optimization = True
+            with (
+                base.program_guard(sharding_program, sharding_startup_program),
+                base.unique_name.guard(),
+            ):
+                input_x = paddle.static.data(
+                    name="x", shape=[None, 32], dtype='float32'
+                )
+                input_y = paddle.static.data(
+                    name="y", shape=[None, 1], dtype='int64'
+                )
+                cost = self.mlp(input_x=input_x, input_y=input_y)
+                output_name = cost.name
+                optimizer = fleet.distributed_optimizer(
+                    paddle.optimizer.Adam(), strategy
+                )
+                optimizer.minimize(cost)
+
+            trainer_id = fleet.worker_index()
+            exe = paddle.static.Executor(get_device_place(trainer_id))
+            rank = fleet.worker_index()
+            exe.run(sharding_startup_program)
+            exe.run(program=sharding_program, feed=self.gen_data())
+
+
+if __name__ == "__main__":
+    unittest.main()