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244 lines
9.1 KiB
244 lines
9.1 KiB
# Copyright (c) 2020 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 os
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import logging
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import warnings
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import paddle.fluid as fluid
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from paddle.fluid import core
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from .runtime_base import RuntimeBase
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class ParameterServerRuntime(RuntimeBase):
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def __init__(self):
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super(ParameterServerRuntime, self).__init__()
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self._communicator = None
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def _set_basic_info(self, context):
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self.context = context
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self.role_maker = context["role_maker"]
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self.origin_main_program = context["origin_main_program"]
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self.origin_startup_program = context["origin_startup_program"]
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self.async_strategy = self._get_distributed_strategy()
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self.compiled_strategy = self.build_compiled_startegy()
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def _get_distributed_strategy(self):
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strategy = None
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
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dist_strategy = self.context["valid_strategy"]
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k_steps = dist_strategy.a_sync_configs["k_steps"]
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if not dist_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_sync_strategy()
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if dist_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_async_strategy()
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if dist_strategy.a_sync and k_steps > 0:
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strategy = StrategyFactory.create_geo_strategy(k_steps)
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if not strategy:
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raise ValueError("k_steps must be invalid value, please check")
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return strategy
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def build_compiled_startegy(self):
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import CompileTimeStrategy
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compiled_config = CompileTimeStrategy(
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self.origin_main_program, self.origin_main_program,
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self.async_strategy, self.role_maker)
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return compiled_config
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def _load_sparse_params(self, dirname, varnames):
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from paddle.fluid.communicator import LargeScaleKV
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
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scale_kv = LargeScaleKV()
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for varname in varnames:
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origin_varname, _, _ = _get_varname_parts(varname)
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sparse_dir = os.path.join(dirname, origin_varname, varname)
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scale_kv.load(varname, sparse_dir)
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@staticmethod
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def __exclude_vars(exclude_var_names=[]):
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def is_valid(var):
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if var.name in exclude_var_names:
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return False
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
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origin_varname, _, _ = _get_varname_parts(var.name)
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if origin_varname.endswith("@GRAD"):
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return False
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if origin_varname == "learning_rate_0":
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return False
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if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
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var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
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var.desc.type() == core.VarDesc.VarType.READER:
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return False
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return var.persistable
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return is_valid
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def _init_worker(self):
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def sync_strategy_envs():
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kwargs = {}
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kwargs["pserver_endpoints"] = self.role_maker.get_pserver_endpoints(
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)
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kwargs["trainer_id"] = self.role_maker.worker_index()
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return kwargs
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def geo_strategy_envs():
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import get_sparse_tablenames
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def get_sparse_attrs():
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opt_init_map = {}
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opt_init_map["gaussian_random"] = ["seed", "mean", "std"]
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opt_init_map["fill_constant"] = ["value"]
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opt_init_map["uniform_random"] = ["seed", "min", "max"]
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opt_init_map[
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"truncated_gaussian_random"] = ["seed", "mean", "std"]
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dist_varnames = get_sparse_tablenames(self.origin_main_program,
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True)
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sparse_varnames = get_sparse_tablenames(
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self.origin_main_program, False)
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if len(dist_varnames) != 0:
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raise ValueError(
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"GeoStrategy can not support large scale embeding now, please use fluid.layers.embedding"
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)
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init_attrs = []
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for value_name in sparse_varnames:
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value_var = self.origin_main_program.global_block().vars[
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value_name]
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value_attr = [
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value_name,
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",".join([str(dim) for dim in value_var.shape])
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]
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for op in self.origin_startup_program.global_block().ops:
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if op.type in opt_init_map.keys(
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) and value_name == op.output("Out")[0]:
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init_attr = [op.type]
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for attr in opt_init_map[op.type]:
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init_attr.append(str(op.attr(attr)))
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value_attr.append("&".join(init_attr))
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init_attrs.append(":".join(value_attr))
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break
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return "#".join(init_attrs)
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kwargs = {}
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kwargs["trainers"] = self.role_maker.worker_num()
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kwargs["sparse_attrs"] = get_sparse_attrs()
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return kwargs
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_lr_ops
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import \
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SyncStrategy, GeoStrategy
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trainer_config = self.async_strategy.get_trainer_runtime_config()
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lrs = _get_lr_ops(self.origin_main_program)
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if len(lrs) > 0:
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kwargs = {"need_global_step": "1"}
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else:
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kwargs = {"need_global_step": "0"}
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if isinstance(self.async_strategy, GeoStrategy):
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geo_kwargs = geo_strategy_envs()
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kwargs.update(geo_kwargs)
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if isinstance(self.async_strategy, SyncStrategy):
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sync_kwargs = sync_strategy_envs()
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kwargs.update(sync_kwargs)
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kwargs = kwargs if kwargs else None
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send_ctx = self.compiled_strategy.get_communicator_send_context()
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if self.compiled_strategy.is_geo_mode():
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recv_ctx = self.compiled_strategy.get_communicator_recv_context(
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recv_type=4)
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else:
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recv_ctx = self.compiled_strategy.get_communicator_recv_context(
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recv_type=1)
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from paddle.fluid.communicator import Communicator
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self._communicator = Communicator(
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trainer_config.mode, kwargs,
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trainer_config.get_communicator_flags())
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self._communicator.init_with_ctx(send_ctx, recv_ctx)
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if not self._communicator.is_running():
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self._communicator.start()
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else:
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warnings.warn("communicator has been initialized, skip")
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def _init_server(self, *args, **kwargs):
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if len(args) > 1:
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raise ValueError("init server can only accept 1 args: `dirname`")
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elif len(args) == 1:
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model_dirname = args[0]
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else:
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model_dirname = None
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executor = fluid.Executor(fluid.CPUPlace())
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executor.run(fluid.default_startup_program())
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if not model_dirname:
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return
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if not os.path.isdir(model_dirname):
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raise ValueError("There is no directory named '%s'", model_dirname)
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sparse_varnames = self.compiled_strategy.get_sparse_varname_on_ps(True)
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distribtued_varnames = self.compiled_strategy.get_sparse_varname_on_ps(
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False)
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remaining_vars = list(
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filter(
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ParameterServerRuntime.__exclude_vars(sparse_varnames +
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distribtued_varnames),
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fluid.default_main_program().list_vars()))
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fluid.io.load_vars(
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executor,
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main_program=fluid.default_main_program(),
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dirname=model_dirname,
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vars=remaining_vars)
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self._load_sparse_params(
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dirname=model_dirname, varnames=sparse_varnames)
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# todo(tangwei12) load distributed vars
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# self._load_sparse_params(dirname=model_dir, varnames=distribtued_varnames)
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def _run_server(self):
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executor = fluid.Executor(fluid.CPUPlace())
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executor.run(fluid.default_main_program())
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def _stop_worker(self):
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self._communicator.stop()
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executor = fluid.Executor(fluid.CPUPlace())
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executor.close()
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