【paddle.fleet】Feature/fleet ps api 2.0 (#25857)
* add paddle.fleet.AsyncOptimizer Co-authored-by: dongdaxiang <dongdaxiang@baidu.com>revert-24895-update_cub
tangwei12
5 years ago
committed by
GitHub
parent
3c8daa9b89
commit
c14ec8782b
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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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from paddle import fluid
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from paddle.fluid import compiler
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from .async_optimizer import AsyncMetaOptimizer
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class AsyncGraphExecutionOptimizer(AsyncMetaOptimizer):
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def __init__(self, optimizer):
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super(AsyncGraphExecutionOptimizer, 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 _can_apply(self):
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k_steps = self.user_defined_strategy.a_sync_configs["k_steps"]
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if k_steps < 0:
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return False
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if self.role_maker.is_server():
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return False
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return True
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def _is_graph_out(self):
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return True
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def _try_to_compile(self, main_program, loss):
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dist_strategy = self._get_distributed_strategy()
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build_strategy = dist_strategy.get_build_strategy()
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exec_strategy = dist_strategy.get_execute_strategy()
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self._compiled_program = compiler.CompiledProgram(main_program)
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self._compiled_program.with_data_parallel(
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loss_name=loss.name,
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build_strategy=build_strategy,
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exec_strategy=exec_strategy,
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share_vars_from=None)
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return self._compiled_program
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def minimize(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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program = loss.block.program
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compiled_program = self._try_to_compile(program, loss)
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program._graph = compiled_program
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# just return self.optimizer_ops and self.param_grads
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return None, None
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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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from paddle import fluid
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from .meta_optimizer_base import MetaOptimizerBase
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class AsyncMetaOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(AsyncMetaOptimizer, 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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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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main_program, startup_program = \
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self._build_trainer_programs(compiled_config) if self.role_maker.is_worker() \
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else self._build_pserver_programs(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["k_steps"] = -1
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@ -0,0 +1,243 @@
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# 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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@ -0,0 +1,113 @@
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# 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");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import time
|
||||
import unittest
|
||||
|
||||
import paddle
|
||||
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
|
||||
|
||||
|
||||
class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
|
||||
def setUp(self):
|
||||
os.environ["PADDLE_PSERVER_NUMS"] = "2"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["POD_IP"] = "127.0.0.1"
|
||||
os.environ["PADDLE_PORT"] = "36001"
|
||||
os.environ["PADDLE_TRAINER_ID"] = "0"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
|
||||
"127.0.0.1:36001,127.0.0.2:36001"
|
||||
|
||||
def test_a_sync_optimizer_trainer(self):
|
||||
os.environ["TRAINING_ROLE"] = "TRAINER"
|
||||
import paddle.fleet as fleet
|
||||
|
||||
main_program = paddle.fluid.Program()
|
||||
startup_program = paddle.fluid.Program()
|
||||
|
||||
paddle.fluid.framework.switch_main_program(main_program)
|
||||
paddle.fluid.framework.switch_startup_program(startup_program)
|
||||
|
||||
fleet.init(role_maker.PaddleCloudRoleMaker())
|
||||
input_x = paddle.fluid.layers.data(
|
||||
name="x", shape=[32], dtype='float32')
|
||||
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
|
||||
|
||||
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
strategy = paddle.fleet.DistributedStrategy()
|
||||
strategy.a_sync = True
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.01)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
prog = paddle.fluid.default_main_program()
|
||||
self.assertNotEqual(prog.global_block().ops[-1].type, "send_barrier")
|
||||
|
||||
sends = 0
|
||||
sgds = 0
|
||||
for op in prog.global_block().ops:
|
||||
if op.type == "send":
|
||||
sends += 1
|
||||
if op.type == "sgd":
|
||||
sgds += 1
|
||||
self.assertEqual(sends, 7)
|
||||
self.assertEqual(sgds, 0)
|
||||
|
||||
fleet.init_worker()
|
||||
time.sleep(8)
|
||||
fleet.stop_worker()
|
||||
|
||||
def test_a_sync_optimizer_pserver(self):
|
||||
os.environ["TRAINING_ROLE"] = "PSERVER"
|
||||
import paddle.fleet as fleet
|
||||
|
||||
main_program = paddle.fluid.Program()
|
||||
startup_program = paddle.fluid.Program()
|
||||
|
||||
paddle.fluid.framework.switch_main_program(main_program)
|
||||
paddle.fluid.framework.switch_startup_program(startup_program)
|
||||
|
||||
fleet.init(role_maker.PaddleCloudRoleMaker())
|
||||
input_x = paddle.fluid.layers.data(
|
||||
name="x", shape=[32], dtype='float32')
|
||||
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
|
||||
|
||||
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
strategy = paddle.fleet.DistributedStrategy()
|
||||
strategy.a_sync = True
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.01)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
prog = paddle.fluid.default_main_program()
|
||||
self.assertEqual(prog.global_block().ops[0].type, "listen_and_serv")
|
||||
fleet.init_server()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -0,0 +1,110 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
import paddle
|
||||
import os
|
||||
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
|
||||
import time
|
||||
|
||||
|
||||
class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
|
||||
def setUp(self):
|
||||
os.environ["PADDLE_PSERVER_NUMS"] = "2"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["POD_IP"] = "127.0.0.1"
|
||||
os.environ["PADDLE_PORT"] = "36001"
|
||||
os.environ["PADDLE_TRAINER_ID"] = "0"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
|
||||
"127.0.0.1:36001,127.0.0.2:36001"
|
||||
|
||||
def test_a_sync_optimizer_trainer(self):
|
||||
os.environ["TRAINING_ROLE"] = "TRAINER"
|
||||
import paddle.fleet as fleet
|
||||
|
||||
main_program = paddle.fluid.Program()
|
||||
startup_program = paddle.fluid.Program()
|
||||
|
||||
paddle.fluid.framework.switch_main_program(main_program)
|
||||
paddle.fluid.framework.switch_startup_program(startup_program)
|
||||
|
||||
fleet.init(role_maker.PaddleCloudRoleMaker())
|
||||
input_x = paddle.fluid.layers.data(
|
||||
name="x", shape=[32], dtype='float32')
|
||||
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
|
||||
|
||||
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
strategy = paddle.fleet.DistributedStrategy()
|
||||
strategy.a_sync = True
|
||||
strategy.a_sync_configs = {"k_steps": 100}
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.01)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
prog = paddle.fluid.default_main_program()
|
||||
self.assertEqual(prog.global_block().ops[-1].type, "send")
|
||||
|
||||
sends = 0
|
||||
sgds = 0
|
||||
|
||||
for op in prog.global_block().ops:
|
||||
if op.type == "send":
|
||||
sends += 1
|
||||
if op.type == "sgd":
|
||||
sgds += 1
|
||||
self.assertEqual(sends, 1)
|
||||
self.assertEqual(sgds, 6)
|
||||
|
||||
def test_a_sync_optimizer_pserver(self):
|
||||
os.environ["TRAINING_ROLE"] = "PSERVER"
|
||||
import paddle.fleet as fleet
|
||||
|
||||
main_program = paddle.fluid.Program()
|
||||
startup_program = paddle.fluid.Program()
|
||||
|
||||
paddle.fluid.framework.switch_main_program(main_program)
|
||||
paddle.fluid.framework.switch_startup_program(startup_program)
|
||||
|
||||
fleet.init(role_maker.PaddleCloudRoleMaker())
|
||||
input_x = paddle.fluid.layers.data(
|
||||
name="x", shape=[32], dtype='float32')
|
||||
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
|
||||
|
||||
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
strategy = paddle.fleet.DistributedStrategy()
|
||||
strategy.a_sync = True
|
||||
strategy.a_sync_configs = {"k_steps": 100}
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.01)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
prog = paddle.fluid.default_main_program()
|
||||
self.assertEqual(prog.global_block().ops[0].type, "listen_and_serv")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@ -0,0 +1,73 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
import paddle
|
||||
import os
|
||||
import paddle.fleet as fleet
|
||||
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
|
||||
import time
|
||||
|
||||
|
||||
class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
|
||||
def setUp(self):
|
||||
os.environ["PADDLE_PSERVER_NUMS"] = "2"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["POD_IP"] = "127.0.0.1"
|
||||
os.environ["PADDLE_PORT"] = "6007"
|
||||
os.environ["TRAINING_ROLE"] = "TRAINER"
|
||||
os.environ["PADDLE_TRAINER_ID"] = "0"
|
||||
os.environ["PADDLE_TRAINERS_NUM"] = "2"
|
||||
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
|
||||
"127.0.0.1:36001,127.0.0.2:36001"
|
||||
|
||||
def test_gradient_merge_optimizer(self):
|
||||
fleet.init(role_maker.PaddleCloudRoleMaker())
|
||||
input_x = paddle.fluid.layers.data(
|
||||
name="x", shape=[32], dtype='float32')
|
||||
input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
|
||||
|
||||
fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
|
||||
fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
|
||||
prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
|
||||
cost = paddle.fluid.layers.cross_entropy(
|
||||
input=prediction, label=input_y)
|
||||
avg_cost = paddle.fluid.layers.mean(x=cost)
|
||||
|
||||
strategy = paddle.fleet.DistributedStrategy()
|
||||
strategy.a_sync = False
|
||||
optimizer = paddle.optimizer.SGD(learning_rate=0.01)
|
||||
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
||||
optimizer.minimize(avg_cost)
|
||||
|
||||
prog = paddle.fluid.default_main_program()
|
||||
self.assertEqual(prog.global_block().ops[-1].type, "send_barrier")
|
||||
|
||||
sends = 0
|
||||
sgds = 0
|
||||
for op in prog.global_block().ops:
|
||||
if op.type == "send":
|
||||
sends += 1
|
||||
if op.type == "sgd":
|
||||
sgds += 1
|
||||
self.assertEqual(sends, 6)
|
||||
self.assertEqual(sgds, 0)
|
||||
|
||||
fleet.init_worker()
|
||||
time.sleep(8)
|
||||
fleet.stop_worker()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue