chore: import upstream snapshot with attribution

test/collective/fleet/parallel_dygraph_control_flow_same.py

@@ -0,0 +1,86 @@
+# 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 numpy as np
+from legacy_test.test_dist_base import (
+    TestParallelDyGraphRunnerBase,
+    runtime_main,
+)
+
+import paddle
+from paddle.nn import Linear
+
+np.random.seed(2021)
+paddle.seed(1024)
+
+batch_size = 4
+batch_num = 1000
+
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.net_a = paddle.nn.Sequential(
+            paddle.nn.Linear(10, 20),
+            paddle.nn.Linear(20, 20),
+            paddle.nn.Linear(20, 5),
+        )
+        self.net_b = paddle.nn.Sequential(
+            paddle.nn.Linear(10, 20),
+            paddle.nn.Linear(20, 20),
+            paddle.nn.Linear(20, 5),
+        )
+        self.net_unused = Linear(10, 20)
+        self.step = 0
+
+    def forward(self, x):
+        if self.step % 2 == 0:
+            return self.net_a(x)
+        else:
+            return self.net_b(x)
+
+        self.step = self.step + 1
+
+
+def fake_sample_reader():
+    def __reader__():
+        for i in range(batch_num):
+            x_data = np.random.random_sample((10,)).astype('float32')
+            yield x_data
+
+    return __reader__
+
+
+class TestSimpleNet(TestParallelDyGraphRunnerBase):
+    def get_model(self):
+        model = SimpleNet()
+        train_reader = paddle.batch(
+            fake_sample_reader(), batch_size=batch_size, drop_last=True
+        )
+        optimizer = paddle.optimizer.SGD(
+            learning_rate=0.001, parameters=model.parameters()
+        )
+        return model, train_reader, optimizer
+
+    def run_one_loop(self, model, optimizer, batch):
+        x_data = np.array(list(batch))
+        x_data = x_data.reshape((-1, 10))
+        x = paddle.to_tensor(x_data)
+        out = model(x)
+        loss = out.sum() / len(batch)
+        return loss
+
+
+if __name__ == "__main__":
+    runtime_main(TestSimpleNet)