wehub-resource-sync
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# Copyright (c) 2020 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 numpy as np
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from op_test import get_device_place
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import paddle
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from paddle import base
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from paddle.nn import Linear
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class MLP(paddle.nn.Layer):
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def __init__(self, param_attr=None, bias_attr=None):
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super().__init__()
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self._linear1 = Linear(784, 10)
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self._linear2 = Linear(10, 10)
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def forward(self, inputs):
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y = self._linear1(inputs)
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y = self._linear2(y)
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return y
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class TestDataParallelStateDict(unittest.TestCase):
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def test_data_parallel_state_dict(self):
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with base.dygraph.guard():
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paddle.distributed.init_parallel_env()
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mlp = MLP()
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parallel_mlp = paddle.DataParallel(mlp)
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single_state = mlp.state_dict()
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parallel_state = parallel_mlp.state_dict()
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base_para = {}
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place = get_device_place()
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for k, v in single_state.items():
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self.assertTrue(k in parallel_state)
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np.testing.assert_array_equal(
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v.numpy(), parallel_state[k].numpy()
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)
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base_para[k] = v.numpy()
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for k, v in parallel_state.items():
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np_t = v.numpy()
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var = v.value().get_tensor()
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var.set(np.zeros_like(np_t), place)
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self.assertTrue(np.sum(np.abs(v.numpy())) == 0)
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parallel_mlp.set_dict(base_para)
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parallel_state = parallel_mlp.state_dict()
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for k, v in parallel_state.items():
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np.testing.assert_array_equal(v.numpy(), base_para[k])
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parallel_mlp.load_dict(base_para)
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if __name__ == '__main__':
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unittest.main()
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