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127 lines
4.8 KiB
127 lines
4.8 KiB
# 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 paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid import compiler
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import numpy as np
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import unittest
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import os
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import sys
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import math
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def simple_fc_net():
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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 TestPassBuilder(unittest.TestCase):
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def check_network_convergence(self, use_cuda, build_strategy=None):
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os.environ['CPU_NUM'] = str(4)
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = simple_fc_net()
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test_program = main.clone(for_test=True)
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opt = fluid.optimizer.SGD(learning_rate=0.001)
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opt.minimize(loss)
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batch_size = 32
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image = np.random.normal(size=(batch_size, 784)).astype('float32')
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label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup)
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feed_dict = {'image': image, 'label': label}
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train_cp = compiler.CompiledProgram(main).with_data_parallel(
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loss_name=loss.name, build_strategy=build_strategy)
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test_cp = compiler.CompiledProgram(test_program).with_data_parallel(
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loss_name=loss.name,
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build_strategy=build_strategy,
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share_vars_from=train_cp)
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for i in range(5):
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_ = exe.run(train_cp, fetch_list=[loss.name], feed=feed_dict)
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test_loss, = exe.run(test_cp,
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fetch_list=[loss.name],
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feed=feed_dict)
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train_loss = exe.run(train_cp,
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fetch_list=[loss.name],
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feed=feed_dict)
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avg_test_loss_val = np.array(test_loss).mean()
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if math.isnan(float(avg_test_loss_val)):
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sys.exit("got NaN loss, testing failed.")
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avg_train_loss_val = np.array(train_loss).mean()
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if math.isnan(float(avg_train_loss_val)):
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sys.exit("got NaN loss, training failed.")
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self.assertTrue(
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np.allclose(
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train_loss, test_loss, atol=1e-8),
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"Train loss: " + str(train_loss) + "\n Test loss:" +
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str(test_loss))
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def test_parallel_testing_with_new_strategy(self):
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build_strategy = fluid.BuildStrategy()
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self.assertFalse(build_strategy.fuse_elewise_add_act_ops)
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build_strategy.fuse_elewise_add_act_ops = True
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pass_builder = build_strategy._finalize_strategy_and_create_passes()
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self.assertTrue("fuse_elewise_add_act_pass" in
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[p.type() for p in pass_builder.all_passes()])
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origin_len = len(pass_builder.all_passes())
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viz_pass = pass_builder.append_pass("graph_viz_pass")
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self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
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pass_builder.insert_pass(
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len(pass_builder.all_passes()), "graph_viz_pass")
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self.assertEqual(origin_len + 2, len(pass_builder.all_passes()))
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pass_builder.remove_pass(len(pass_builder.all_passes()) - 1)
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self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
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viz_pass.set("graph_viz_path", "/tmp/test_viz_pass")
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self.check_network_convergence(
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use_cuda=core.is_compiled_with_cuda(),
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build_strategy=build_strategy)
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try:
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os.stat("/tmp/test_viz_pass")
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except os.error:
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self.assertFalse(True)
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if __name__ == '__main__':
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unittest.main()
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