integrated HALF_ASYNC to communicator (#21869)
* add half_async in the communicator * fix DistributedStrategyrevert-22710-feature/integrated_ps_api
tangwei12
6 years ago
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# Copyright (c) 2019 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 time
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import threading
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import numpy
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.communicator import Communicator
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from paddle.fluid.transpiler.distribute_transpiler import DistributedMode
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
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from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
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class TestCommunicator(unittest.TestCase):
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def net(self):
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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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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return avg_cost
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def test_communicator_geo(self):
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role = role_maker.UserDefinedRoleMaker(
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current_id=0,
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role=role_maker.Role.WORKER,
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worker_num=2,
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server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
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fleet.init(role)
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avg_cost = self.net()
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optimizer = fluid.optimizer.SGD(0.01)
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strategy = DistributeTranspilerConfig()
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strategy.sync_mode = False
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strategy.runtime_split_send_recv = True
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strategy.geo_sgd_mode = True
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strategy.wait_port = False
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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fleet.init_worker()
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time.sleep(10)
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fleet.stop_worker()
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# class TestCommunicatorGEO(unittest.TestCase):
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# def test_communicator_init_and_start(self):
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# prog = fluid.Program()
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# envs = {}
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# envs["communicator_thread_pool_size"] = "5"
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# envs["communicator_send_wait_times"] = "5"
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# kwargs = {}
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# kwargs["push_vars"] = {}
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# kwargs["trainers"] = 10
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# kwargs["push_nums"] = 10
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# comm = Communicator(prog, DistributedMode.GEO, kwargs, envs)
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2019 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 os
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import sys
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import time
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import threading
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import subprocess
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import unittest
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import numpy
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.communicator import Communicator
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from paddle.fluid.transpiler.distribute_transpiler import DistributedMode
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from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
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class TestCommunicatorHalfAsyncEnd2End(unittest.TestCase):
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def net(self):
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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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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return avg_cost, x, y
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def fake_reader(self):
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def reader():
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for i in range(10000):
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x = numpy.random.random((1, 13)).astype('float32')
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y = numpy.random.randint(0, 2, (1, 1)).astype('int64')
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yield x, y
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return reader
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def run_pserver(self, role, strategy):
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fleet.init(role)
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avg_cost, x, y = self.net()
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optimizer = fluid.optimizer.SGD(0.01)
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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fleet.init_server()
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fleet.run_server()
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def run_trainer(self, role, strategy):
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place = fluid.core.CPUPlace()
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exe = fluid.Executor(place)
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fleet.init(role)
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avg_cost, x, y = self.net()
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optimizer = fluid.optimizer.SGD(0.01)
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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exe.run(fleet.startup_program)
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fleet.init_worker()
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train_reader = paddle.batch(self.fake_reader(), batch_size=24)
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feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
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for batch_id, data in enumerate(train_reader()):
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exe.run(fleet.main_program, feed=feeder.feed(data), fetch_list=[])
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fleet.stop_worker()
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def run_ut(self):
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strategy = DistributeTranspilerConfig()
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strategy.sync_mode = False
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strategy.runtime_split_send_recv = True
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strategy.half_async = True
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training_role = os.getenv("TRAINING_ROLE", "TRAINER")
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role = role_maker.UserDefinedRoleMaker(
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current_id=0,
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role=role_maker.Role.WORKER
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if training_role == "TRAINER" else role_maker.Role.SERVER,
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worker_num=2,
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server_endpoints=["127.0.0.1:6002"])
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if training_role == "TRAINER":
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self.run_trainer(role, strategy)
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else:
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self.run_pserver(role, strategy)
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def test_communicator(self):
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run_server_cmd = """
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from __future__ import print_function
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import sys
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import os
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import time
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import threading
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import subprocess
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import unittest
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import numpy
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.communicator import Communicator
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from paddle.fluid.communicator import DistributedMode
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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from test_communicator_half_async import TestCommunicatorHalfAsyncEnd2End
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from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
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class RunServer(TestCommunicatorHalfAsyncEnd2End):
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def runTest(self):
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pass
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os.environ["TRAINING_ROLE"] = "PSERVER"
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half_run_server = RunServer()
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half_run_server.run_ut()
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"""
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server_file = "run_server_for_communicator_haflaysnc.py"
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with open(server_file, "w") as wb:
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wb.write(run_server_cmd)
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os.environ["TRAINING_ROLE"] = "PSERVER"
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_python = sys.executable
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ps_cmd = "{} {}".format(_python, server_file)
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ps_proc = subprocess.Popen(
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ps_cmd.strip().split(" "),
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE)
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["FLAGS_communicator_send_queue_size"] = "1"
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os.environ["FLAGS_communicator_max_merge_var_num"] = "1"
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self.run_ut()
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ps_proc.kill()
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if os.path.exists(server_file):
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os.remove(server_file)
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# class TestCommunicatorHalfAsync2(unittest.TestCase):
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# def test_communicator_init_and_start(self):
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# prog = fluid.Program()
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# envs = {}
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# envs["communicator_send_queue_size"] = "12"
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# envs["communicator_max_merge_var_num"] = "12"
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# envs["communicator_thread_pool_size"] = "5"
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# envs["communicator_send_wait_times"] = "5"
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# comm = Communicator(prog, DistributedMode.HALF_ASYNC, None, envs)
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# comm.start()
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# time.sleep(10)
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# comm.stop()
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if __name__ == '__main__':
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unittest.main()
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