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146 lines
4.5 KiB
146 lines
4.5 KiB
# 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 .executor import global_scope
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"""
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Communicator is used for async distribute training in distribute_transpiler mode.
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It's a wrapper of a cpp class Communicator and should be used inside fleet API.
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"""
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from . import core
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from .framework import Program
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from .transpiler.distribute_transpiler import DistributedMode
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__all__ = ['Communicator']
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class Communicator(object):
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def __init__(self, program, mode, kwargs=None, envs={}):
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"""
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Communicator is used for async distribute training in distribute_transpiler mode.
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It's a wrapper of a cpp class Communicator and should be used inside fleet API.
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Args:
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program(Program): the trainers program after transpile of distribute_transpiler.
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It's used by communicator to extract the information to do communication.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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prog = fluid.Program()
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comm = fluid.communicator.Communicator(prog)
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comm.start()
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comm.stop()
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"""
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# set all recv op to not_run mode
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assert isinstance(program, Program)
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for op in program.block(0).ops:
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if op.type == "recv":
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op._set_attr('do_not_run', True)
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if mode == DistributedMode.GEO:
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push_vars = kwargs["push_vars"]
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push_var_names = []
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for k, vs in push_vars.items():
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varnames = "&".join(vs["var_names"])
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sections = "&".join([str(v) for v in vs["sections"]])
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endpoints = "&".join(vs["epmap"])
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is_sparse = "1" if vs["is_sparse"] == ['True'] else "0"
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push_var_names.append(k)
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envs[k] = "#".join([varnames, sections, endpoints, is_sparse])
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envs["geo_trainer_nums"] = str(kwargs["trainers"])
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envs["geo_need_push_nums"] = str(kwargs["push_nums"])
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envs["geo_send_varnames"] = '#'.join(push_var_names)
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if mode == DistributedMode.SYNC:
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envs["pserver_endpoints"] = ','.join(kwargs["pserver_endpoints"])
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envs["trainer_id"] = str(kwargs["trainer_id"])
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mode_str = None
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if mode == DistributedMode.SYNC:
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mode_str = "SYNC"
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elif mode == DistributedMode.ASYNC:
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mode_str = "ASYNC"
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elif mode == DistributedMode.HALF_ASYNC:
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mode_str = "HALF_ASYNC"
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elif mode == DistributedMode.GEO:
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mode_str = "GEO"
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self.communicator_ = core.DistCommunicator(mode_str, program.desc,
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global_scope(), envs)
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def start(self):
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"""
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Start communicator. Should call before training process.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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prog = fluid.Program()
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comm = fluid.communicator.Communicator(prog)
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comm.start()
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comm.stop()
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"""
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self.communicator_.start()
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def stop(self):
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"""
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Stop communicator. Should call after training process.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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prog = fluid.Program()
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comm = fluid.communicator.Communicator(prog)
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comm.start()
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comm.stop()
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"""
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self.communicator_.stop()
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def is_running(self):
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"""
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Get communicator is running or stop.
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Returns:
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bool
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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prog = fluid.Program()
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comm = fluid.communicator.Communicator(prog)
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comm.is_running()
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"""
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self.communicator_.is_running()
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