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83 lines
2.5 KiB
83 lines
2.5 KiB
# 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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from __future__ import print_function
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import contextlib
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import unittest
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import numpy as np
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import six
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import unittest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dygraph
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from paddle.fluid.dygraph.nn import Linear
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import paddle.fluid.core as core
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class MLP(fluid.Layer):
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def __init__(self, param_attr=None, bias_attr=None):
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super(MLP, self).__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 fluid.dygraph.guard():
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strategy = paddle.distributed.prepare_context()
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mlp = MLP()
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parallel_mlp = dygraph.parallel.DataParallel(mlp, strategy)
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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 = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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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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self.assertTrue(
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np.array_equal(v.numpy(), parallel_state[k].numpy()))
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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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self.assertTrue(np.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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