wehub-resource-sync
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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import paddle.distributed as dist
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import paddle.optimizer as opt
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from paddle import nn
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class LinearNet(nn.Layer):
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def __init__(self):
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super().__init__()
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self._linear1 = nn.Linear(10, 10)
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self._linear2 = nn.Linear(10, 1)
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def forward(self, x):
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return self._linear2(self._linear1(x))
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def train(print_result=False):
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# 1. initialize parallel environment
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dist.init_parallel_env()
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# 2. create data parallel layer & optimizer
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layer = LinearNet()
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dp_layer = paddle.DataParallel(layer)
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loss_fn = nn.MSELoss()
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adam = opt.Adam(learning_rate=0.001, parameters=dp_layer.parameters())
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# 3. run layer
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inputs = paddle.randn([10, 10], 'float32')
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outputs = dp_layer(inputs)
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labels = paddle.randn([10, 1], 'float32')
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loss = loss_fn(outputs, labels)
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if print_result is True:
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print("loss:", loss.numpy())
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loss.backward()
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print("Grad is", layer._linear1.weight.grad)
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adam.step()
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adam.clear_grad()
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class TestSpawn(unittest.TestCase):
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def test_spawn(self):
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dist.spawn(train, backend='gloo', nprocs=4)
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def test_wrong_backend(self):
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try:
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dist.spawn(train, backend='something', nprocs=4)
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except ValueError as e:
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self.assertEqual(type(e), ValueError)
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if __name__ == '__main__':
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unittest.main()
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