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276 lines
11 KiB
276 lines
11 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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from paddle.fluid import core
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import subprocess
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import re
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import platform
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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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if self.user_defined_strategy.auto == True:
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return True
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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 _try_auto_apply_geo(self, program, compiled_config):
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def get_sys_free_mem():
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plat = platform.system()
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if platform.system() == "Darwin":
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vm = subprocess.Popen(
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['vm_stat'], stdout=subprocess.PIPE).communicate()[0]
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# Process vm_stat
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vmLines = vm.split('\n')
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sep = re.compile(':[\s]+')
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vmStats = {}
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for row in range(1, len(vmLines) - 2):
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rowText = vmLines[row].strip()
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rowElements = sep.split(rowText)
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vmStats[(rowElements[0]
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)] = int(rowElements[1].strip('\.')) * 4096
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return vmStats["Pages free"]
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elif platform.system() == "Linux":
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mems = {}
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with open('/proc/meminfo', 'rb') as f:
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for line in f:
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fields = line.split()
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mems[fields[0]] = int(fields[1]) * 1024
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free = mems[b'MemFree:']
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return free
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else:
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raise ValueError(
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"%s platform is unsupported is parameter server optimizer" %
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(platform.system()))
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if self.user_defined_strategy.auto == False:
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return
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a_sync_configs = self.user_defined_strategy.a_sync_configs
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if a_sync_configs["k_steps"] >= 0:
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return
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self.user_defined_strategy.a_sync = True
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if not isinstance(self.inner_opt, fluid.optimizer.SGDOptimizer):
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# auto async
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a_sync_configs["k_steps"] = 0
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self.user_defined_strategy.a_sync_configs = a_sync_configs
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return
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from paddle.fluid.incubate.fleet.parameter_server.ir.vars_metatools import dtype_to_size
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free = get_sys_free_mem()
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param_grad_pairs = compiled_config.origin_sparse_pairs + compiled_config.origin_dense_pairs
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processed_var_names = set(["@EMPTY@"])
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param_memory_size = 0
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for param_grad_pair in param_grad_pairs:
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param, grad = param_grad_pair
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param_memory_size += param.m_size
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processed_var_names.add(param.name)
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upper_mem_use = param_memory_size * 5.0
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program_tmp_vars = dict()
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batch_size = 1024
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for op in program.global_block().ops:
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for var_name in op.output_arg_names:
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if var_name in processed_var_names:
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continue
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processed_var_names.add(var_name)
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var = program.global_block().vars[var_name]
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if var.desc.type() != core.VarDesc.VarType.LOD_TENSOR:
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continue
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data_count = 1
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neg_dim_count = 0
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for x in var.shape:
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if x < 0:
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if neg_dim_count >= 1:
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raise ValueError(
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"Var %s has more than one negative dim." %
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(var_name))
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neg_dim_count += 1
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data_count *= (-x)
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else:
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data_count *= x
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program_tmp_vars[var_name] = (data_count, neg_dim_count,
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dtype_to_size[var.dtype])
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for varname in program_tmp_vars:
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data_count, neg_dim_count, type_size = program_tmp_vars[varname]
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if neg_dim_count == 1:
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data_count *= batch_size
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var_memory = data_count * type_size
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upper_mem_use += var_memory
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if upper_mem_use < free:
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# auto geo
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a_sync_configs["k_steps"] = 800
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else:
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# auto async
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a_sync_configs["k_steps"] = 0
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self.user_defined_strategy.a_sync_configs = a_sync_configs
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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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_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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None, self.role_maker)
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self._try_auto_apply_geo(_origin_main_program, compiled_config)
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strategy = self._get_distributed_strategy()
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compiled_config.strategy = strategy
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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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dist_strategy.a_sync_configs = {}
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self.user_defined_strategy.a_sync_configs = {}
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def _enable_strategy(self, dist_strategy):
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dist_strategy.a_sync = True
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dist_strategy.a_sync_configs = {}
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