Merge pull request #15716 from Yancey1989/refine_pg
Refine ParallelGraph Executionrevert-15774-anakin_subgraph_engine
Xin Pan
6 years ago
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GitHub
commit
6019054cdd
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# Copyright (c) 2018 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 unittest
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import numpy as np
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import os
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os.environ['FLAGS_enable_parallel_graph'] = str(1)
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import paddle.fluid.core as core
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import os
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import paddle.fluid as fluid
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from parallel_executor_test_base import TestParallelExecutorBase
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def simple_fc_net(use_feed):
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img = fluid.layers.data(name='image', shape=[784], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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hidden = img
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for _ in range(4):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='tanh',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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class TestMNIST(TestParallelExecutorBase):
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@classmethod
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def setUpClass(cls):
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os.environ['CPU_NUM'] = str(4)
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def _init_data(self):
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np.random.seed(5)
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img = np.random.random(size=[32, 784]).astype(np.float32)
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label = np.ones(shape=[32, 1], dtype='int64')
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return img, label
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# simple_fc
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def check_simple_fc_convergence(self, use_cuda, use_reduce=False):
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if use_cuda and not core.is_compiled_with_cuda():
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return
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img, label = self._init_data()
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self.check_network_convergence(
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simple_fc_net,
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feed_dict={"image": img,
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"label": label},
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use_cuda=use_cuda,
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use_reduce=use_reduce)
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def test_simple_fc(self):
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# use_cuda
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self.check_simple_fc_convergence(True)
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def check_simple_fc_parallel_accuracy(self, use_cuda):
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if use_cuda and not core.is_compiled_with_cuda():
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return
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img, label = self._init_data()
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single_first_loss, single_last_loss = self.check_network_convergence(
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method=simple_fc_net,
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seed=1,
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feed_dict={"image": img,
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"label": label},
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use_cuda=use_cuda,
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use_parallel_executor=False)
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parallel_first_loss, parallel_last_loss = self.check_network_convergence(
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method=simple_fc_net,
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seed=1,
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feed_dict={"image": img,
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"label": label},
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use_cuda=use_cuda,
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use_parallel_executor=True)
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self.assertAlmostEquals(
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np.mean(parallel_first_loss),
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single_first_loss,
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delta=1e-6, )
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self.assertAlmostEquals(
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np.mean(parallel_last_loss), single_last_loss, delta=1e-6)
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def test_simple_fc_parallel_accuracy(self):
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self.check_simple_fc_parallel_accuracy(True)
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if __name__ == '__main__':
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unittest.main()
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