chore: import upstream snapshot with attribution

test/legacy_test/test_cpuonly_spawn.py

@@ -0,0 +1,71 @@
+# 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 paddle
+import paddle.distributed as dist
+import paddle.optimizer as opt
+from paddle import nn
+
+
+class LinearNet(nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self._linear1 = nn.Linear(10, 10)
+        self._linear2 = nn.Linear(10, 1)
+
+    def forward(self, x):
+        return self._linear2(self._linear1(x))
+
+
+def train(print_result=False):
+    # 1. initialize parallel environment
+    dist.init_parallel_env()
+
+    # 2. create data parallel layer & optimizer
+    layer = LinearNet()
+    dp_layer = paddle.DataParallel(layer)
+
+    loss_fn = nn.MSELoss()
+    adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
+
+    # 3. run layer
+    inputs = paddle.randn([10, 10], 'float32')
+    outputs = dp_layer(inputs)
+    labels = paddle.randn([10, 1], 'float32')
+    loss = loss_fn(outputs, labels)
+
+    if print_result is True:
+        print("loss:", loss.numpy())
+
+    loss.backward()
+    print("Grad is", layer._linear1.weight.grad)
+    adam.step()
+    adam.clear_grad()
+
+
+class TestSpawn(unittest.TestCase):
+    def test_spawn(self):
+        dist.spawn(train, backend='gloo', nprocs=4)
+
+    def test_wrong_backend(self):
+        try:
+            dist.spawn(train, backend='something', nprocs=4)
+        except ValueError as e:
+            self.assertEqual(type(e), ValueError)
+
+
+if __name__ == '__main__':
+    unittest.main()