chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 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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import copy
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import os
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import platform
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import re
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import subprocess
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import paddle.distributed.passes
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from paddle.distributed.passes import PassContext
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from paddle.distributed.ps.utils.ps_factory import PsProgramBuilderFactory
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from paddle.distributed.ps.utils.public import (
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TrainerRuntimeConfig,
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build_var_distributed,
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dtype_to_size,
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get_dist_env,
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get_var_mem_size,
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logger,
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)
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from paddle.framework import core
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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().__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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"RecomputeOptimizer",
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"AMPOptimizer",
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"LarsOptimizer",
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"LambOptimizer",
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"ASPOptimizer",
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]
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def _set_basic_info(
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self, loss, role_maker, user_defined_optimizer, user_defined_strategy
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):
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super()._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy
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)
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def _set_origin_programs(self, losses):
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self.origin_main_programs = []
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for loss in losses:
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self.origin_main_programs.append(loss.block.program)
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def _init_ps_pass_context(self, loss, startup_program):
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self.pass_ctx = PassContext()
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attrs = {}
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# trainer
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attrs["env"] = get_dist_env()
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attrs['loss'] = loss
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attrs['min_block_size'] = 81920
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attrs['origin_main_program'] = loss.block.program
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attrs['origin_startup_program'] = startup_program
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attrs['origin_main_programs'] = self.origin_main_programs
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attrs['cloned_main'] = attrs['origin_main_program'].clone()
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attrs['cloned_startup'] = attrs['origin_startup_program'].clone()
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attrs['user_defined_strategy'] = self.user_defined_strategy
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attrs['valid_strategy'] = self.user_defined_strategy
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attrs['trainer'] = TrainerRuntimeConfig(self.user_defined_strategy)
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attrs['ps_mode'] = attrs['trainer'].mode
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logger.info("ps_mode: {}".format(attrs['ps_mode']))
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attrs['role_maker'] = self.role_maker
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attrs['is_heter_ps_mode'] = (
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self.role_maker._is_heter_parameter_server_mode
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)
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attrs['is_worker'] = self.role_maker._is_worker()
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attrs['is_server'] = self.role_maker._is_server()
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attrs['is_heter_worker'] = self.role_maker._is_heter_worker()
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logger.info(
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"this process is heter? {}".format(attrs['is_heter_worker'])
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)
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attrs['use_ps_gpu'] = self.user_defined_strategy.a_sync_configs[
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"use_ps_gpu"
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]
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attrs['use_gpu_graph'] = self.user_defined_strategy.a_sync_configs[
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"use_gpu_graph"
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]
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attrs['lr_decay_steps'] = self.user_defined_strategy.a_sync_configs[
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"lr_decay_steps"
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]
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# FL
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attrs['local_sparse'] = attrs[
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"user_defined_strategy"
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].trainer_desc_configs["local_sparse"]
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attrs['remote_sparse'] = attrs[
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"user_defined_strategy"
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].trainer_desc_configs["remote_sparse"]
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attrs['is_fl_ps_mode'] = self.user_defined_strategy.is_fl_ps_mode
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attrs['with_coordinator'] = (
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self.user_defined_strategy.is_with_coordinator
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)
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attrs['k_steps'] = self.user_defined_strategy.a_sync_configs["k_steps"]
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attrs['launch_barrier'] = self.user_defined_strategy.a_sync_configs[
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"launch_barrier"
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]
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attrs['launch_barrier_flag'] = int(
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os.getenv("FLAGS_LAUNCH_BARRIER", "1")
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)
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build_var_distributed(attrs)
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# server
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attrs['_main_server'] = paddle.static.Program()
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attrs['_startup_server'] = paddle.static.Program()
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attrs['tensor_table'] = {}
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self.pass_ctx._attrs = attrs
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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 minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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optimize_ops, params_grads = self.inner_opt.minimize(
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loss, startup_program, parameter_list, no_grad_set
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)
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if startup_program is None:
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startup_program = paddle.static.default_startup_program()
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# print("program after inner optimizer minimize:",
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# str(loss.block.program))
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self._set_origin_programs([loss])
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self._init_ps_pass_context(loss, startup_program)
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ps_builder = PsProgramBuilderFactory()._create_ps_program_builder(
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self.pass_ctx
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)
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ps_builder._build_programs()
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return optimize_ops, params_grads
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def minimize_losses_impl(
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self,
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losses,
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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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):
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self.inner_opts = [self.inner_opt]
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for idx, loss in enumerate(losses):
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if idx == 0:
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continue
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tmp_opt = copy.deepcopy(self.inner_opt)
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self.inner_opts.append(tmp_opt)
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if parameter_list is None:
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parameter_list = [None] * len(losses)
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for idx, loss in enumerate(losses):
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startup_prog = startup_program[idx]
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parameters = parameter_list[idx]
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self.inner_opts[idx].minimize(
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loss, startup_prog, parameters, no_grad_set
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)
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self._set_origin_programs(losses)
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for idx, loss in enumerate(losses):
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print("ps_optimizer idx loss:", idx, loss)
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startup_prog = startup_program[idx]
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self._init_ps_pass_context(loss, startup_prog)
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ps_builder = PsProgramBuilderFactory()._create_ps_program_builder(
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self.pass_ctx
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)
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ps_builder._build_programs()
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startup_program[idx] = self.pass_ctx._attrs['cloned_startup']
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return None, None
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def _can_apply_geo(self, program):
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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
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).communicate()[0]
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# Process vm_stat
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vmLines = vm.split('\n')
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sep = re.compile(r':[\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(r'\.')) * 4096
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)
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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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f"{platform.system()} platform is unsupported is parameter server optimizer"
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)
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if not isinstance(self.inner_opt, paddle.optimizer.SGD):
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return False
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free = get_sys_free_mem()
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processed_var_names = {"@EMPTY@"}
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param_memory_size = 0
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for varname in program.global_block().vars:
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var = program.global_block().vars[varname]
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if (
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not var.persistable
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or var.desc.type() != core.VarDesc.VarType.DENSE_TENSOR
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):
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continue
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param_memory_size += get_var_mem_size(var)
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processed_var_names.add(varname)
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upper_mem_use = param_memory_size * 5.0
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program_tmp_vars = {}
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eval_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.DENSE_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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f"Var {var_name} has more than one negative dim."
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)
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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] = (
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data_count,
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neg_dim_count,
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dtype_to_size[var.dtype],
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)
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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 *= eval_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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return True
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else:
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return False
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def _enable_strategy(self, dist_strategy, context):
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a_sync_configs = dist_strategy.a_sync_configs
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if dist_strategy.a_sync_configs["k_steps"] >= 0:
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return
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dist_strategy.a_sync = True
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a_sync_configs = dist_strategy.a_sync_configs
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is_geo = self._can_apply_geo(context["origin_main_program"])
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a_sync_configs["k_steps"] = 800 if is_geo else 0
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dist_strategy.a_sync_configs = a_sync_configs
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def _disable_strategy(self, dist_strategy):
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dist_strategy.a_sync = False
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a_sync_configs = dist_strategy.a_sync_configs
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dist_strategy.a_sync_configs["k_steps"] = -1
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dist_strategy.a_sync_configs = a_sync_configs
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