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161 lines
7.1 KiB
161 lines
7.1 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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from paddle import fluid
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from .meta_optimizer_base import MetaOptimizerBase
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class ParameterServerOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(ParameterServerOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = []
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def _is_graph_out(self):
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return False
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def _can_apply(self):
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if self.role_maker._is_collective:
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return False
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k_steps = self.user_defined_strategy.a_sync_configs["k_steps"]
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return True if k_steps >= 0 else False
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def _get_distributed_strategy(self):
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
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k_steps = self.user_defined_strategy.a_sync_configs["k_steps"]
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strategy = None
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if not self.user_defined_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_sync_strategy()
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if self.user_defined_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_async_strategy()
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if self.user_defined_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_trainer_programs(self, compiled_config):
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from paddle.fluid.incubate.fleet.parameter_server.ir import trainer_pass as worker
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_main = compiled_config.origin_main_program.clone()
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_startup = compiled_config.origin_startup_program.clone()
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if not compiled_config.is_geo_mode():
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# for main program
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_main = worker.delete_optimizer_pass(_main, compiled_config)
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_main = worker.distributed_ops_pass(_main, compiled_config)
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_main = worker.append_send_ops_pass(_main, compiled_config)
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# for startup program
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_startup = worker.fake_init_ops_pass(_startup, compiled_config)
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_startup = worker.init_from_server_pass(_startup, compiled_config)
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_startup = worker.delet_extra_optimizes_pass(_startup,
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compiled_config)
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# for heter program
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if self.role_maker._is_heter_parameter_server_mode:
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from paddle.fluid.incubate.fleet.parameter_server.ir import heter_trainer_pass as heter_worker
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if self.role_maker._is_heter_worker():
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# for heter worker
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_main = heter_worker.split_heter_worker_ops_pass(
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_main, compiled_config)
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else:
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# for default worker
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_main = heter_worker.split_trainer_ops_pass(_main,
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compiled_config)
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# for startup change
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_startup = heter_worker.delete_startup_useless_ops_var_pass(
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_startup, _main, compiled_config)
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else:
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_main = worker.append_send_ops_pass(_main, compiled_config)
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_startup = _startup
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return _main, _startup
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def _build_pserver_programs(self, compiled_config):
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from paddle.fluid.incubate.fleet.parameter_server.ir import pserver_pass as server
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_main = fluid.Program()
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_startup = fluid.Program()
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if not compiled_config.is_geo_mode():
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_main = server.add_listen_and_serv_pass(_main, compiled_config)
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_main = server.add_rpc_global_flags_pass(_main, compiled_config)
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_main = server.add_optimizer_pass(_main, compiled_config)
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_main = server.large_scale_sparse_pass(_main, _main,
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compiled_config, False)
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_startup = server.build_pserver_startup_program_pass(
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_startup, _main, compiled_config)
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_startup = server.large_scale_sparse_pass(_startup, _main,
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compiled_config, True)
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if not compiled_config.is_sync_mode():
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_main = server.delete_unused_in_main_pass(_main,
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compiled_config)
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_startup = server.delete_unused_in_startup_pass(_startup, _main,
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compiled_config)
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else:
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_main = server.add_listen_and_serv_pass(_main, compiled_config)
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_main = server.add_rpc_global_flags_pass(_main, compiled_config)
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_main = server.add_geo_optimizer_pass(_main, compiled_config)
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_main = server.large_scale_sparse_pass(_main, _main,
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compiled_config, False)
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_startup = server.build_pserver_startup_program_pass(
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_startup, _main, compiled_config)
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_startup = server.large_scale_sparse_pass(_startup, _main,
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compiled_config, True)
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_startup = server.delete_unused_in_startup_pass(_startup, _main,
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compiled_config)
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return _main, _startup
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def minimize_impl(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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self.inner_opt.minimize(loss, startup_program, parameter_list,
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no_grad_set)
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strategy = self._get_distributed_strategy()
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_origin_main_program = loss.block.program
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_origin_startup_program = startup_program
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from paddle.fluid.incubate.fleet.parameter_server.ir import public as public
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compiled_config = public.CompileTimeStrategy(_origin_main_program,
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_origin_startup_program,
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strategy, self.role_maker)
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if self.role_maker.is_worker() or self.role_maker._is_heter_worker():
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main_program, startup_program = self._build_trainer_programs(
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compiled_config)
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elif self.role_maker.is_server():
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main_program, startup_program = self._build_pserver_programs(
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compiled_config)
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loss.block.program = main_program
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fluid.framework.switch_startup_program(startup_program)
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return None, None
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def _disable_strategy(self, dist_strategy):
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self.user_defined_strategy.a_sync_configs = {}
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