Bug fix for sparse recorder (#21969)
* test=develop, bug fix for sparse recorderrevert-22710-feature/integrated_ps_api
123malin
6 years ago
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GitHub
parent
7e2af4c91f
commit
985bceac53
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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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import unittest
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import paddle.fluid as fluid
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import gc
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gc.set_debug(gc.DEBUG_COLLECTABLE)
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class TranspilerTest(unittest.TestCase):
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def setUp(self):
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self.trainer_id = 0
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self.trainers = 2
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self.pservers = 2
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# NOTE: we do not actually bind this port
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self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
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self.pserver1_ep = "127.0.0.1:6174"
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self.pserver2_ep = "127.0.0.1:6175"
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self.sync_mode = True
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self.transpiler = None
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def net_conf(self):
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x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
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y_predict = fluid.layers.fc(input=x,
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size=1000,
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act=None,
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param_attr=fluid.ParamAttr(name='fc_w'),
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bias_attr=fluid.ParamAttr(name='fc_b'))
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
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sgd_optimizer.minimize(avg_cost)
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def get_main_program(self):
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main = fluid.Program()
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main.random_seed = 1
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with fluid.program_guard(main):
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self.net_conf()
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self.origin_prog = main.clone()
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return main
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def get_trainer(self, config=None, sync_mode=True):
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src = fluid.default_startup_program().clone()
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t = self._transpiler_instance(config, sync_mode=True)
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trainer_main = t.get_trainer_program(wait_port=False)
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trainer_startup = fluid.default_startup_program()
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assert (src.num_blocks == 1)
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assert (trainer_startup.num_blocks == src.num_blocks)
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return trainer_main, trainer_startup
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def get_pserver(self, ep, config=None, sync_mode=True):
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t = self._transpiler_instance(config, sync_mode)
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pserver = t.get_pserver_program(ep)
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startup = t.get_startup_program(ep, pserver)
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return pserver, startup
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def _transpiler_instance(self, config=None, sync_mode=True):
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if not self.transpiler:
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main = self.get_main_program()
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self.transpiler = fluid.DistributeTranspiler(config=config)
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self.transpiler.transpile(
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self.trainer_id,
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program=main,
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pservers=self.pserver_eps,
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trainers=self.trainers,
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sync_mode=sync_mode)
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return self.transpiler
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def transpiler_test_impl(self):
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pass
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def test_transpiler(self):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, startup):
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self.transpiler_test_impl()
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# NOTE: run gc.collect to eliminate pybind side objects to
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# prevent random double-deallocate when inherited in python.
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del self.transpiler
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del main
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del startup
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gc.collect()
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class TestBasicModelAsync(TranspilerTest):
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def transpiler_test_impl(self):
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config = fluid.DistributeTranspilerConfig()
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config.sync_mode = False
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config.runtime_split_send_recv = True
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pserver, startup = self.get_pserver(self.pserver1_ep, config, False)
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pserver2, startup2 = self.get_pserver(self.pserver2_ep, config, False)
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trainer, _ = self.get_trainer(config, False)
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self.assertEqual([op.type for op in trainer.global_block().ops], [
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'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
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'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
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'elementwise_add_grad', 'send', 'mul_grad', 'send', 'recv', 'recv'
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])
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self.assertEqual(len(pserver.blocks), 3)
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# block0: listen_and_serv
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self.assertEqual([op.type for op in pserver.blocks[0].ops],
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["listen_and_serv"])
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self.assertEqual(pserver.blocks[0].ops[0].attr("distributed_mode"), 1)
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# block1~2: optimize pass
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self.assertEqual([op.type for op in pserver.blocks[2].ops], ["sgd"])
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class TestBasicModelHalfAsync(TranspilerTest):
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def transpiler_test_impl(self):
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config = fluid.DistributeTranspilerConfig()
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config.sync_mode = False
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config.runtime_split_send_recv = False
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pserver, startup = self.get_pserver(self.pserver1_ep, config, False)
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pserver2, startup2 = self.get_pserver(self.pserver2_ep, config, False)
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trainer, _ = self.get_trainer(config, False)
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self.assertEqual([op.type for op in trainer.global_block().ops], [
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'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
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'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
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'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
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'recv', 'recv', 'concat'
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])
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self.assertEqual(len(pserver.blocks), 3)
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# block0: listen_and_serv
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self.assertEqual([op.type for op in pserver.blocks[0].ops],
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["listen_and_serv"])
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self.assertEqual(pserver.blocks[0].ops[0].attr("distributed_mode"), 2)
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# block1~2: optimize pass
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self.assertEqual([op.type for op in pserver.blocks[2].ops], ["sgd"])
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class TestBasicModelSync(TranspilerTest):
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def transpiler_test_impl(self):
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config = fluid.DistributeTranspilerConfig()
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config.sync_mode = True
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config.runtime_split_send_recv = False
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pserver, startup = self.get_pserver(self.pserver1_ep, config, True)
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pserver2, startup2 = self.get_pserver(self.pserver2_ep, config, True)
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trainer, _ = self.get_trainer(config, True)
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self.assertEqual([op.type for op in trainer.global_block().ops], [
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'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
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'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
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'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
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'send_barrier', 'recv', 'recv', 'fetch_barrier', 'concat'
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])
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self.assertEqual(len(pserver.blocks), 3)
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# block0: listen_and_serv
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self.assertEqual([op.type for op in pserver.blocks[0].ops],
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["listen_and_serv"])
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self.assertEqual(pserver.blocks[0].ops[0].attr("distributed_mode"), 0)
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# block1~2: optimize pass
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self.assertEqual([op.type for op in pserver.blocks[2].ops],
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["sum", "scale", "sgd"])
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if __name__ == "__main__":
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unittest.main()
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