paddlepaddle--paddle/python/paddle/distributed/fleet/meta_optimizers/ps_optimizer.py

#   Copyright (c) 2022 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

import copy
import os
import platform
import re
import subprocess

import paddle.distributed.passes
from paddle.distributed.passes import PassContext
from paddle.distributed.ps.utils.ps_factory import PsProgramBuilderFactory
from paddle.distributed.ps.utils.public import (
    TrainerRuntimeConfig,
    build_var_distributed,
    dtype_to_size,
    get_dist_env,
    get_var_mem_size,
    logger,
)
from paddle.framework import core

from .meta_optimizer_base import MetaOptimizerBase


class ParameterServerOptimizer(MetaOptimizerBase):
    def __init__(self, optimizer):
        super().__init__(optimizer)
        self.inner_opt = optimizer
        # we do not allow meta optimizer to be inner optimizer currently
        self.meta_optimizers_white_list = [
            "RecomputeOptimizer",
            "AMPOptimizer",
            "LarsOptimizer",
            "LambOptimizer",
            "ASPOptimizer",
        ]

    def _set_basic_info(
        self, loss, role_maker, user_defined_optimizer, user_defined_strategy
    ):
        super()._set_basic_info(
            loss, role_maker, user_defined_optimizer, user_defined_strategy
        )

    def _set_origin_programs(self, losses):
        self.origin_main_programs = []
        for loss in losses:
            self.origin_main_programs.append(loss.block.program)

    def _init_ps_pass_context(self, loss, startup_program):
        self.pass_ctx = PassContext()
        attrs = {}
        # trainer
        attrs["env"] = get_dist_env()

        attrs['loss'] = loss
        attrs['min_block_size'] = 81920
        attrs['origin_main_program'] = loss.block.program
        attrs['origin_startup_program'] = startup_program

        attrs['origin_main_programs'] = self.origin_main_programs

        attrs['cloned_main'] = attrs['origin_main_program'].clone()
        attrs['cloned_startup'] = attrs['origin_startup_program'].clone()

        attrs['user_defined_strategy'] = self.user_defined_strategy
        attrs['valid_strategy'] = self.user_defined_strategy
        attrs['trainer'] = TrainerRuntimeConfig(self.user_defined_strategy)
        attrs['ps_mode'] = attrs['trainer'].mode
        logger.info("ps_mode: {}".format(attrs['ps_mode']))
        attrs['role_maker'] = self.role_maker
        attrs['is_heter_ps_mode'] = (
            self.role_maker._is_heter_parameter_server_mode
        )
        attrs['is_worker'] = self.role_maker._is_worker()
        attrs['is_server'] = self.role_maker._is_server()
        attrs['is_heter_worker'] = self.role_maker._is_heter_worker()
        logger.info(
            "this process is heter? {}".format(attrs['is_heter_worker'])
        )
        attrs['use_ps_gpu'] = self.user_defined_strategy.a_sync_configs[
            "use_ps_gpu"
        ]
        attrs['use_gpu_graph'] = self.user_defined_strategy.a_sync_configs[
            "use_gpu_graph"
        ]
        attrs['lr_decay_steps'] = self.user_defined_strategy.a_sync_configs[
            "lr_decay_steps"
        ]
        # FL
        attrs['local_sparse'] = attrs[
            "user_defined_strategy"
        ].trainer_desc_configs["local_sparse"]
        attrs['remote_sparse'] = attrs[
            "user_defined_strategy"
        ].trainer_desc_configs["remote_sparse"]
        attrs['is_fl_ps_mode'] = self.user_defined_strategy.is_fl_ps_mode
        attrs['with_coordinator'] = (
            self.user_defined_strategy.is_with_coordinator
        )

        attrs['k_steps'] = self.user_defined_strategy.a_sync_configs["k_steps"]
        attrs['launch_barrier'] = self.user_defined_strategy.a_sync_configs[
            "launch_barrier"
        ]

        attrs['launch_barrier_flag'] = int(
            os.getenv("FLAGS_LAUNCH_BARRIER", "1")
        )

        build_var_distributed(attrs)

        # server
        attrs['_main_server'] = paddle.static.Program()
        attrs['_startup_server'] = paddle.static.Program()
        attrs['tensor_table'] = {}

        self.pass_ctx._attrs = attrs

    def _is_graph_out(self):
        return False

    def _can_apply(self):
        if self.role_maker._is_collective:
            return False

        k_steps = self.user_defined_strategy.a_sync_configs["k_steps"]
        return True if k_steps >= 0 else False

    def minimize_impl(
        self, loss, startup_program=None, parameter_list=None, no_grad_set=None
    ):
        optimize_ops, params_grads = self.inner_opt.minimize(
            loss, startup_program, parameter_list, no_grad_set
        )
        if startup_program is None:
            startup_program = paddle.static.default_startup_program()
        #        print("program after inner optimizer minimize:",
        #              str(loss.block.program))
        self._set_origin_programs([loss])
        self._init_ps_pass_context(loss, startup_program)
        ps_builder = PsProgramBuilderFactory()._create_ps_program_builder(
            self.pass_ctx
        )
        ps_builder._build_programs()
        return optimize_ops, params_grads

    def minimize_losses_impl(
        self,
        losses,
        startup_program=None,
        parameter_list=None,
        no_grad_set=None,
    ):
        self.inner_opts = [self.inner_opt]
        for idx, loss in enumerate(losses):
            if idx == 0:
                continue
            tmp_opt = copy.deepcopy(self.inner_opt)
            self.inner_opts.append(tmp_opt)
        if parameter_list is None:
            parameter_list = [None] * len(losses)
        for idx, loss in enumerate(losses):
            startup_prog = startup_program[idx]
            parameters = parameter_list[idx]
            self.inner_opts[idx].minimize(
                loss, startup_prog, parameters, no_grad_set
            )
        self._set_origin_programs(losses)
        for idx, loss in enumerate(losses):
            print("ps_optimizer idx loss:", idx, loss)
            startup_prog = startup_program[idx]
            self._init_ps_pass_context(loss, startup_prog)
            ps_builder = PsProgramBuilderFactory()._create_ps_program_builder(
                self.pass_ctx
            )
            ps_builder._build_programs()
            startup_program[idx] = self.pass_ctx._attrs['cloned_startup']
        return None, None

    def _can_apply_geo(self, program):
        def get_sys_free_mem():
            plat = platform.system()
            if platform.system() == "Darwin":
                vm = subprocess.Popen(
                    ['vm_stat'], stdout=subprocess.PIPE
                ).communicate()[0]
                # Process vm_stat
                vmLines = vm.split('\n')
                sep = re.compile(r':[\s]+')
                vmStats = {}
                for row in range(1, len(vmLines) - 2):
                    rowText = vmLines[row].strip()
                    rowElements = sep.split(rowText)
                    vmStats[(rowElements[0])] = (
                        int(rowElements[1].strip(r'\.')) * 4096
                    )
                return vmStats["Pages free"]
            elif platform.system() == "Linux":
                mems = {}
                with open('/proc/meminfo', 'rb') as f:
                    for line in f:
                        fields = line.split()
                        mems[fields[0]] = int(fields[1]) * 1024
                free = mems[b'MemFree:']
                return free
            else:
                raise ValueError(
                    f"{platform.system()} platform is unsupported is parameter server optimizer"
                )

        if not isinstance(self.inner_opt, paddle.optimizer.SGD):
            return False

        free = get_sys_free_mem()
        processed_var_names = {"@EMPTY@"}
        param_memory_size = 0
        for varname in program.global_block().vars:
            var = program.global_block().vars[varname]
            if (
                not var.persistable
                or var.desc.type() != core.VarDesc.VarType.DENSE_TENSOR
            ):
                continue
            param_memory_size += get_var_mem_size(var)
            processed_var_names.add(varname)

        upper_mem_use = param_memory_size * 5.0

        program_tmp_vars = {}
        eval_batch_size = 1024
        for op in program.global_block().ops:
            for var_name in op.output_arg_names:
                if var_name in processed_var_names:
                    continue
                processed_var_names.add(var_name)
                var = program.global_block().vars[var_name]

                if var.desc.type() != core.VarDesc.VarType.DENSE_TENSOR:
                    continue

                data_count = 1
                neg_dim_count = 0
                for x in var.shape:
                    if x < 0:
                        if neg_dim_count >= 1:
                            raise ValueError(
                                f"Var {var_name} has more than one negative dim."
                            )
                        neg_dim_count += 1
                        data_count *= -x
                    else:
                        data_count *= x
                program_tmp_vars[var_name] = (
                    data_count,
                    neg_dim_count,
                    dtype_to_size[var.dtype],
                )

        for varname in program_tmp_vars:
            data_count, neg_dim_count, type_size = program_tmp_vars[varname]
            if neg_dim_count == 1:
                data_count *= eval_batch_size
            var_memory = data_count * type_size
            upper_mem_use += var_memory

        if upper_mem_use < free:
            return True
        else:
            return False

    def _enable_strategy(self, dist_strategy, context):
        a_sync_configs = dist_strategy.a_sync_configs
        if dist_strategy.a_sync_configs["k_steps"] >= 0:
            return
        dist_strategy.a_sync = True
        a_sync_configs = dist_strategy.a_sync_configs

        is_geo = self._can_apply_geo(context["origin_main_program"])

        a_sync_configs["k_steps"] = 800 if is_geo else 0
        dist_strategy.a_sync_configs = a_sync_configs

    def _disable_strategy(self, dist_strategy):
        dist_strategy.a_sync = False
        a_sync_configs = dist_strategy.a_sync_configs
        dist_strategy.a_sync_configs["k_steps"] = -1
        dist_strategy.a_sync_configs = a_sync_configs