chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,450 @@
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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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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
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from paddle.framework import core
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from ..base.private_helper_function import wait_server_ready
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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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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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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# self.micro_batch_size = user_defined_strategy.pipeline_configs[
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# 'micro_batch_size']
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self.num_microbatches = user_defined_strategy.pipeline_configs[
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'accumulate_steps'
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]
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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_dist_env(self):
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trainer_id = int(os.getenv('PADDLE_TRAINER_ID', '0'))
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trainer_endpoints = ''
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current_endpoint = ''
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num_trainers = 0
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if os.getenv('PADDLE_TRAINER_ENDPOINTS'):
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trainer_endpoints = os.getenv('PADDLE_TRAINER_ENDPOINTS')
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current_endpoint = trainer_endpoints.split(',')[trainer_id]
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num_trainers = len(trainer_endpoints.split(','))
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return {
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'trainer_id': trainer_id,
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'num_trainers': num_trainers,
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'current_endpoint': current_endpoint,
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'trainer_endpoints': trainer_endpoints,
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}
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def _get_distributed_strategy(self):
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from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
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StrategyFactory,
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)
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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.incubate.distributed.fleet.parameter_server.ir import (
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trainer_pass as worker,
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)
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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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use_ps_gpu = self.user_defined_strategy.a_sync_configs["use_ps_gpu"]
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if not compiled_config.is_geo_mode():
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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_add_lr_decay_table_pass,
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)
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_add_lr_decay_table_pass(
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_main,
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compiled_config,
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self.user_defined_strategy.a_sync_configs["lr_decay_steps"],
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)
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# for main program
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_main = worker.distributed_ops_pass(
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_main, compiled_config, use_ps_gpu
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)
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if not use_ps_gpu:
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_main = worker.delete_optimizer_pass(_main, compiled_config)
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_main = worker.append_send_ops_pass(_main, compiled_config)
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_startup = worker.delete_extra_optimizes_pass(
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_startup, compiled_config
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)
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# for startup program
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_startup = worker.fake_init_ops_pass(_startup, compiled_config)
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if use_ps_gpu:
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_main = worker.ps_gpu_pass(_main)
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from paddle.distributed.transpiler.collective import (
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SingleProcessMultiThread,
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)
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t = SingleProcessMultiThread()
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env = self.get_dist_env()
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t.transpile(
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startup_program=_startup,
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main_program=_main,
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rank=env["trainer_id"],
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endpoints=env["trainer_endpoints"],
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current_endpoint=env['current_endpoint'],
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wait_port=False,
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)
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compiled_config.set_origin_ps_main_program(_main)
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compiled_config.set_origin_ps_startup_program(_startup)
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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.incubate.distributed.fleet.parameter_server.ir import (
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heter_trainer_pass as heter_worker,
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)
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if self.role_maker._is_heter_worker():
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# for heter worker
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stage_id = self.role_maker._get_stage_id()
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device = self.role_maker._heter_device_type().lower()
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_main = heter_worker.split_heter_worker_ops_pass(
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_main, compiled_config, stage_id, device
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)
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else:
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# for default worker
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_main = heter_worker.split_trainer_ops_pass(
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_main, compiled_config
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)
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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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compiled_config.set_origin_ps_main_program(_main)
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compiled_config.set_origin_ps_startup_program(_startup)
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launch_barrier = self.user_defined_strategy.a_sync_configs[
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"launch_barrier"
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]
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launch_barrier_flag = int(os.getenv("FLAGS_LAUNCH_BARRIER", "1"))
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if launch_barrier and launch_barrier_flag:
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# for trainer wait server ready
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wait_server_ready(self.role_maker._get_pserver_endpoints())
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# for ps-heter mode, wait heter worker ready
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# if self.role_maker._is_heter_parameter_server_mode and self.role_maker._is_worker(
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# ):
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# wait_server_ready(self.role_maker._get_heter_worker_endpoints())
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return _main, _startup
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def _build_pserver_programs(self, compiled_config):
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_main = paddle.static.Program()
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_startup = paddle.static.Program()
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from paddle.incubate.distributed.fleet.parameter_server.ir import (
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pserver_pass as server,
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)
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if not compiled_config.is_geo_mode():
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_optimize_ops,
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)
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is_sgd_adam = False
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main_program = compiled_config.get_origin_main_program()
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ops = _get_optimize_ops(main_program)
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if len(ops) == 0:
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return _main, _startup
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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_add_lr_decay_table_pass,
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)
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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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_add_lr_decay_table_pass(
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main_program, compiled_config, lr_decay_steps
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)
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for op in ops:
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if op.type in ["sgd", "adam"]:
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is_sgd_adam = True
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break
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if is_sgd_adam:
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return _main, _startup
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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(
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_main, _main, compiled_config, False
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)
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_startup = server.build_pserver_startup_program_pass(
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_startup, _main, compiled_config
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)
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_startup = server.large_scale_sparse_pass(
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_startup, _main, compiled_config, True
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)
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if not compiled_config.is_sync_mode():
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_main = server.delete_unused_in_main_pass(
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_main, compiled_config
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)
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_startup = server.delete_unused_in_startup_pass(
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_startup, _main, compiled_config
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)
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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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_startup = server.build_pserver_startup_program_pass(
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_startup, _main, compiled_config
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)
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_startup = server.delete_unused_in_startup_pass(
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_startup, _main, compiled_config
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)
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return _main, _startup
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def _can_apply_geo(self, dist_strategy, 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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from paddle.incubate.distributed.fleet.parameter_server.ir import (
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vars_metatools,
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)
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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 = vars_metatools.create_var_struct(var)
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param_memory_size += param.m_size
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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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vars_metatools.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 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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self.inner_opt.minimize(
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loss, startup_program, parameter_list, no_grad_set
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)
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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.incubate.distributed.fleet.parameter_server.ir import public
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compiled_config = public.CompileTimeStrategy(
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_origin_main_program,
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_origin_startup_program,
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strategy,
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self.role_maker,
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)
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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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)
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if self.role_maker._is_heter_parameter_server_mode:
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_origin_startup_program._heter_pipeline_opt = {
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"startup_program": startup_program,
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"pipeline_stage": int(self.role_maker._get_stage_id()) - 1,
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"heter_place": self.role_maker._heter_device(),
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}
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loss.block.program._heter_pipeline_opt = {
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"trainer": "HeterPipelineTrainer",
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"device_worker": "HeterSection",
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"trainers": self.role_maker._get_stage_trainers(), # trainer num in each stage
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"trainer_id": int(self.role_maker._role_id()),
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"pipeline_stage": int(self.role_maker._get_stage_id()) - 1,
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"num_pipeline_stages": int(
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self.role_maker._get_num_stage()
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),
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"section_program": main_program,
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"num_microbatches": self.num_microbatches,
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"heter_place": self.role_maker._heter_device(),
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}
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else:
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loss.block.program = main_program
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paddle.framework.switch_startup_program(startup_program)
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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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)
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loss.block.program = main_program
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paddle.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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# if self.role_maker._is_heter_parameter_server_mode:
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# dist_strategy.pipeline = False
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# dist_strategy.pipeline_configs = {
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# "micro_batch_size": 1,
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# "accumulate_steps": 1,
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# }
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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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a_sync_configs["k_steps"] = -1
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dist_strategy.a_sync_configs = a_sync_configs
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def _enable_strategy(self, dist_strategy, context):
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# if self.role_maker._is_heter_parameter_server_mode:
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# dist_strategy.pipeline = True
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# dist_strategy.pipeline_configs = {
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# "micro_batch_size": 1,
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# "accumulate_steps": 1,
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# }
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a_sync_configs = dist_strategy.a_sync_configs
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if 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(
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dist_strategy, context["origin_main_program"]
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)
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if is_geo:
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a_sync_configs["k_steps"] = 800
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else:
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a_sync_configs["k_steps"] = 0
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dist_strategy.a_sync_configs = a_sync_configs
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