# 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 os
import warnings

from paddle import fluid
from paddle.fluid import core
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program

from ..base.private_helper_function import wait_server_ready
from .runtime_base import RuntimeBase

__all__ = []


def conv_indent(indent):
    return "".join([" "] * indent)


PSERVER_SAVE_SUFFIX = ".shard"


def parse_table_class(varname, o_main_program):
    from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
        is_distributed_sparse_op,
        is_sparse_op,
    )

    for op in o_main_program.global_block().ops:
        if not is_distributed_sparse_op(op) and not is_sparse_op(op):
            continue

        param_name = op.input("W")[0]

        if (
            param_name == varname
            and op.type == "lookup_table"
            or op.type == "lookup_table_v2"
        ):
            if op.has_attr('table_class') and op.attr("table_class") != "none":
                return op.attr('table_class')
            else:
                return "MemorySparseTable"


def get_default_accessor_proto(accessor, varname, o_main_program):
    embedding_dim = 0
    for var in o_main_program.list_vars():
        if var.name == varname:
            embedding_dim = var.shape[1]
            break

    if not accessor.HasField("accessor_class"):
        accessor.accessor_class = "CtrCommonAccessor"
    if not accessor.HasField("fea_dim"):
        accessor.fea_dim = embedding_dim
    if not accessor.HasField("embedx_dim"):
        accessor.embedx_dim = embedding_dim - 3
    if not accessor.HasField("embedx_threshold"):
        accessor.embedx_threshold = 0

    ctr_accessor_param = accessor.ctr_accessor_param
    if not ctr_accessor_param.HasField("nonclk_coeff"):
        ctr_accessor_param.nonclk_coeff = 0.1
    if not ctr_accessor_param.HasField("click_coeff"):
        ctr_accessor_param.click_coeff = 1.0
    if not ctr_accessor_param.HasField("base_threshold"):
        ctr_accessor_param.base_threshold = 0
    if not ctr_accessor_param.HasField("delta_threshold"):
        ctr_accessor_param.delta_threshold = 0
    if not ctr_accessor_param.HasField("delta_keep_days"):
        ctr_accessor_param.delta_keep_days = 16
    if not ctr_accessor_param.HasField("show_click_decay_rate"):
        ctr_accessor_param.show_click_decay_rate = 1
    if not ctr_accessor_param.HasField("delete_threshold"):
        ctr_accessor_param.delete_threshold = 0
    if not ctr_accessor_param.HasField("delete_after_unseen_days"):
        ctr_accessor_param.delete_after_unseen_days = 30
    if not ctr_accessor_param.HasField("ssd_unseenday_threshold"):
        ctr_accessor_param.ssd_unseenday_threshold = 1

    for sgd_param in [accessor.embed_sgd_param, accessor.embedx_sgd_param]:
        if not sgd_param.HasField("name"):
            sgd_param.name = "SparseAdaGradSGDRule"
        if (
            sgd_param.name == "SparseAdaGradSGDRule"
            or sgd_param.name == "StdAdaGradSGDRule"
        ):
            if not sgd_param.adagrad.HasField("learning_rate"):
                sgd_param.adagrad.learning_rate = 0.05
            if not sgd_param.adagrad.HasField("initial_g2sum"):
                sgd_param.adagrad.initial_g2sum = 3.0
            if not sgd_param.adagrad.HasField("initial_range"):
                sgd_param.adagrad.initial_range = 0.0001
            if len(sgd_param.adagrad.weight_bounds) == 0:
                sgd_param.adagrad.weight_bounds.extend([-10.0, 10.0])
        if sgd_param.name == "SparseNaiveSGDRule":
            if not sgd_param.naive.HasField("learning_rate"):
                sgd_param.naive.learning_rate = 0.05
            if not sgd_param.naive.HasField("initial_range"):
                sgd_param.naive.initial_range = 0.0001
            if len(sgd_param.naive.weight_bounds) == 0:
                sgd_param.naive.weight_bounds.extend([-10.0, 10.0])
        if sgd_param.name == "SparseAdamSGDRule":
            if not sgd_param.adam.HasField("learning_rate"):
                sgd_param.adam.learning_rate = 0.001
            if not sgd_param.adam.HasField("initial_range"):
                sgd_param.adam.initial_range = 0.0001
            if not sgd_param.adam.HasField("beta1_decay_rate"):
                sgd_param.adam.beta1_decay_rate = 0.9
            if not sgd_param.adam.HasField("beta2_decay_rate"):
                sgd_param.adam.beta2_decay_rate = 0.999
            if not sgd_param.adam.HasField("ada_epsilon"):
                sgd_param.adam.ada_epsilon = 1e-08
            if len(sgd_param.adam.weight_bounds) == 0:
                sgd_param.adam.weight_bounds.extend([-10.0, 10.0])


def check_embedding_dim(accessor, varname, o_main_program):
    embedding_dim = 0
    for var in o_main_program.list_vars():
        if var.name == varname:
            embedding_dim = var.shape[1]
            break
    fea_dim = accessor.fea_dim
    if fea_dim != embedding_dim:
        raise ValueError(
            "The fea_dim is wrong, it will be sparse_embedding_dim: {}, but got {}".format(
                embedding_dim, fea_dim
            )
        )
    embedx_dim = accessor.embedx_dim
    if embedx_dim != embedding_dim - 3:
        raise ValueError(
            "The embedx_dim is wrong, it will be sparse_embedding_dim - 3: {}, but got {}".format(
                embedding_dim - 3, embedx_dim
            )
        )


class Accessor:
    def __init__(self):
        self.accessor_class = ""
        self.optimizer = None
        self.feature_dim = -1
        self.embedding_dim = -1
        self.optimizer = None

    def to_string(self, indent):
        accessor_str = "{}accessor {{{}\n{}}}"
        attrs = ""
        attrs += f"accessor_class: \"{self.accessor_class}\" "
        attrs += f"fea_dim: {self.feature_dim} "
        attrs += f"embedx_dim: {self.embedding_dim} "
        attrs += "\n"
        if self.optimizer is not None:
            attrs += self.optimizer.to_string(indent)
        return accessor_str.format(
            conv_indent(indent), attrs, conv_indent(indent)
        )


class CommonAccessor:
    def __init__(self):
        self.accessor_class = ""
        self.table_name = None
        self.entry = None
        self.attrs = []
        self.params = []
        self.dims = []
        self.trainer_num = 0
        self.sync = "false"
        self.table_num = None
        self.table_dim = None
        self.initializers = []
        self.opt_input_map = {}
        self.opt_attr_map = {}
        self.opt_init_map = {}
        self.define_optimize_map()

    def define_optimize_map(self):
        opt_input_map = {}
        opt_input_map["sgd"] = [("Param", None), ("LearningRate", 1)]
        opt_input_map["adam"] = [
            ("Param", None),
            ("Moment1", None),
            ("Moment2", None),
            ("Beta1Pow", 1),
            ("Beta2Pow", 1),
            ("LearningRate", 1),
        ]
        opt_input_map["adam_d2sum"] = [
            ("Param", None),
            ("D2Sum", None),
            ("G2Sum", None),
            ("Moment", None),
            ("MomentDecayRate", 1),
            ("AdaDecayRate", 1),
            ("AdaEpsilon", 1),
            ("LearningRate", 1),
        ]
        opt_input_map["sum"] = [("Param", None)]
        opt_input_map["naive_adagrad"] = [
            ("Param", None),
            ("G2Sum", 1),
            ("LearningRate", 1),
        ]

        opt_attr_map = {}
        opt_attr_map["sgd"] = []
        opt_attr_map["sum"] = []
        opt_attr_map["naive_adagrad"] = []
        opt_attr_map["adam"] = [
            ("beta1", "f"),
            ("beta2", "f"),
            ("epsilon", "f"),
        ]
        opt_attr_map["adam_d2sum"] = [
            ("beta1", "f"),
            ("beta2", "f"),
            ("epsilon", "f"),
        ]

        opt_init_map = {}
        opt_init_map["gaussian_random"] = ["seed", "mean", "std"]
        opt_init_map["fill_constant"] = ["value"]
        opt_init_map["uniform_random"] = ["seed", "min", "max"]
        opt_init_map["truncated_gaussian_random"] = ["seed", "mean", "std"]

        self.opt_attr_map = opt_attr_map
        self.opt_input_map = opt_input_map
        self.opt_init_map = opt_init_map

    def parse_entry(self, varname, o_main_program):
        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            is_distributed_sparse_op,
            is_sparse_op,
        )

        for op in o_main_program.global_block().ops:
            if not is_distributed_sparse_op(op) and not is_sparse_op(op):
                continue

            param_name = op.input("W")[0]

            if param_name == varname and op.type == "lookup_table":
                self.entry = op.attr('entry')
                break

            if param_name == varname and op.type == "lookup_table_v2":
                self.entry = "none"
                break

    def get_shard(self, total_dim, shard_num, pserver_id):
        # remainder = total_dim % shard_num
        blocksize = int(total_dim / shard_num + 1)

        if blocksize * (pserver_id + 1) <= total_dim:
            return blocksize
        else:
            if blocksize * pserver_id < total_dim:
                return total_dim - blocksize * pserver_id
            else:
                return 0

    def get_initializer_attr(self, value_name, o_startup_program):
        l_in = "&"
        attr_str = ""

        origin_var_name = value_name
        for op in o_startup_program.global_block().ops:
            if (
                op.type in self.opt_init_map.keys()
                and origin_var_name == op.output("Out")[0]
            ):
                init_attr = [op.type]
                for attr in self.opt_init_map[op.type]:
                    init_attr.append(str(op.attr(attr)))
                attr_str = l_in.join(init_attr)
                break
        return attr_str

    def parse_by_optimizer(
        self,
        grad_name,
        is_sparse,
        size,
        single_dim,
        compiled_strategy,
        adam_d2sum,
    ):
        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            _get_optimize_ops,
        )

        param_name = compiled_strategy.grad_name_to_param_name[grad_name]
        main_program, startup_program = compiled_strategy.get_origin_programs()
        pserver_id = compiled_strategy.get_role_id()
        pserver_num = len(compiled_strategy.get_ps_endpoints())
        optimizer_ops = _get_optimize_ops(main_program)
        oop = None

        for op in optimizer_ops:
            if ("Param" in op.input_names) and (
                op.input("Param")[0] == param_name
            ):
                oop = op
                break

        if oop is None:
            raise ValueError(f"can not find optimizer for {grad_name}")

        params = []
        dims = []
        attrs = []
        initializers = []

        self.trainer_num = compiled_strategy.get_trainers()
        self.table_num = size
        self.table_dim = single_dim

        if oop.type != 'adam' and adam_d2sum:
            print('optimization algorithm is not adam, set adam_d2sum False')
            adam_d2sum = False
        print("adam_d2sum:", adam_d2sum)
        if compiled_strategy.is_geo_mode():
            param_varnames = self.opt_input_map["sum"]
            attr_varnames = self.opt_attr_map["sum"]
            self.accessor_class = "sum"
        elif compiled_strategy.use_ps_gpu and is_sparse:
            param_varnames = self.opt_input_map["naive_adagrad"]
            attr_varnames = self.opt_attr_map["naive_adagrad"]
            self.accessor_class = "sgd"
        elif adam_d2sum and not is_sparse:
            param_varnames = self.opt_input_map["adam_d2sum"]
            attr_varnames = self.opt_attr_map["adam_d2sum"]
            self.accessor_class = "adam_d2sum"
        else:
            param_varnames = self.opt_input_map[oop.type]
            attr_varnames = self.opt_attr_map[oop.type]
            self.accessor_class = oop.type

        for formal_name, shape in param_varnames:
            params.append(formal_name)
            if self.accessor_class == "adam_d2sum":
                # for dims
                if shape is None:
                    if is_sparse:
                        shape = single_dim
                    else:
                        shape = self.get_shard(size, pserver_num, pserver_id)
                dims.append(shape)

                # for initializers
                if formal_name == "Param" or formal_name == "LearningRate":
                    param = main_program.global_block().vars[
                        oop.input(formal_name)[0]
                    ]
                    # TODO: for dense learning_rate, can be different from sparse lr
                    if (
                        formal_name == "LearningRate"
                        and param.name != "learning_rate_0"
                    ):
                        warnings.warn("will support decay soon")
                        param = main_program.global_block().vars[
                            "learning_rate_0"
                        ]

                    initializer = self.get_initializer_attr(
                        param.name, startup_program
                    )
                elif formal_name == "MomentDecayRate":
                    initializer = "fill_constant&0.99"
                elif formal_name == "AdaDecayRate":
                    initializer = "fill_constant&0.9999"
                elif formal_name == "AdaEpsilon":
                    initializer = "fill_constant&1.0e-8"
                else:
                    initializer = "fill_constant&0"
                initializers.append(initializer)
            else:
                if formal_name == "G2Sum":
                    dims.append(1)
                    initializer = "fill_constant&0"
                    initializers.append(initializer)
                else:
                    param = main_program.global_block().vars[
                        oop.input(formal_name)[0]
                    ]
                    if (
                        formal_name == "LearningRate"
                        and param.name != "learning_rate_0"
                    ):
                        warnings.warn("will support decay soon")
                        param = main_program.global_block().vars[
                            "learning_rate_0"
                        ]

                    if shape is None:
                        if is_sparse:
                            shape = single_dim
                        else:
                            shape = self.get_shard(
                                size, pserver_num, pserver_id
                            )
                    dims.append(shape)

                    initializer = self.get_initializer_attr(
                        param.name, startup_program
                    )
                    initializers.append(initializer)

        for attr_varname, type_ in attr_varnames:
            value = oop.attr(attr_varname)
            attrs.append("&".join([attr_varname, type_, str(value)]))

        self.params = params
        self.dims = dims
        self.initializers = initializers
        self.attrs = attrs

    def to_string(self, indent):
        accessor_str = "{}common {{{}\n{}}}"
        attrs = ""
        attrs += f"name: \"{self.accessor_class}\" "

        if self.table_name:
            attrs += f"table_name: \"{self.table_name}\" "

        if self.entry:
            attrs += f"entry: \"{self.entry}\" "
        attrs += f"trainer_num: {self.trainer_num} "
        attrs += f"sync: {self.sync} "
        if self.table_num:
            attrs += f"table_num: {self.table_num} "
        if self.table_dim:
            attrs += f"table_dim: {self.table_dim} "

        for param in self.params:
            attrs += f"params: \"{param}\" "

        for dim in self.dims:
            attrs += f"dims: {dim} "

        for initializer in self.initializers:
            attrs += f"initializers: \"{initializer}\" "

        attrs += "\n"
        return accessor_str.format(
            conv_indent(indent), attrs, conv_indent(indent)
        )


class Tensor:
    def __init__(self):
        self.main_program_id = None
        self.startup_program_id = None
        self.feed_var_name = None
        self.fetch_var_name = None
        self.tensor_table_class = False

    def to_string(self, indent):
        program_str = "{}tensor {{{}\n{}}}"
        attrs = ""
        attrs += f"feed_var_name: \"{str(self.feed_var_name)}\" "
        attrs += f"fetch_var_name: \"{str(self.fetch_var_name)}\" "
        attrs += f"startup_program_id: {str(self.startup_program_id)} "
        attrs += f"main_program_id: {str(self.main_program_id)} "
        attrs += "tensor_table_class: \"{}\" ".format(
            str(self.tensor_table_class)
        )
        attrs += "\n"
        return program_str.format(
            conv_indent(indent), attrs, conv_indent(indent)
        )


class Table:
    def __init__(self):
        self.id = -1
        self.table_class = None
        self.shard_num = -1
        self.type = None
        self.accessor = None
        self.common = None
        self.tensor = None
        self.accessor_proto = None

    def to_string(self, indent):
        # if self.id == 1:
        #     proto_txt = ''
        #     with open('./sparse_table.prototxt') as f:
        #         proto_txt = f.read()
        #     return proto_txt
        table_str = "{}downpour_table_param {{{}\n{}}}"

        attrs = ""
        attrs += f"table_id: {self.id} "
        attrs += f"table_class: \"{self.table_class}\" "
        attrs += f"shard_num: {self.shard_num} "
        attrs += f"type: {self.type}"
        attrs += "\n"
        indent += 2

        if self.accessor_proto is not None:
            accessor_str = "{}accessor {{{}\n{}}}"
            accessor_str = accessor_str.format(
                conv_indent(indent), self.accessor_proto, conv_indent(indent)
            )
            attrs += accessor_str + "\n"
        elif self.accessor is not None:
            attrs += self.accessor.to_string(indent)
            attrs += "\n"

        if self.tensor is not None:
            attrs += self.tensor.to_string(indent)
            attrs += "\n"

        if self.common is not None:
            attrs += self.common.to_string(indent)
            attrs += "\n"

        return table_str.format(conv_indent(indent), attrs, conv_indent(indent))


class Service:
    def __init__(self):
        self.server_class = "BrpcPsServer"
        self.client_class = "BrpcPsClient"
        self.service_class = "BrpcPsService"
        self.start_server_port = 0
        self.server_thread_num = 12

    def to_string(self, indent):
        service_str = "{}service_param {{{}\n{}}}"

        attrs = ""
        attrs += f"server_class: \"{self.server_class}\" "
        attrs += f"client_class: \"{self.client_class}\" "
        attrs += f"service_class: \"{self.service_class}\" "
        attrs += f"start_server_port: {self.start_server_port} "
        attrs += f"server_thread_num: {self.server_thread_num} "

        return service_str.format(
            conv_indent(indent), attrs, conv_indent(indent)
        )


class DownpourServer:
    def __init__(self):
        self.service = None
        self.tables = []

    def set_service_param(self, service):
        self.service = service

    def append_tables(self, table):
        if not isinstance(table, Table):
            raise ValueError("only support instance Table")
        self.tables.append(table)

    def to_string(self, indent):
        server_str = "{}downpour_server_param {{{}\n{}}}"

        table_strs = ""
        indent += 2

        table_strs += "\n"
        table_strs += self.service.to_string(indent)

        for table in self.tables:
            table_strs += "\n"
            table_strs += table.to_string(indent)
        return server_str.format(
            conv_indent(indent), table_strs, conv_indent(indent)
        )


class Server:
    def __init__(self):
        self.servers = []

    def add_server(self, server):
        if not isinstance(server, DownpourServer):
            raise ValueError("only support instance DownpourServer")
        self.servers.append(server)

    def __str__(self):
        server_str = "server_param {{{}\n}}"
        indent = 2
        servers_str = ""
        for server in self.servers:
            servers_str += "\n"
            servers_str += server.to_string(indent)

        return server_str.format(servers_str)


class DownpourWorker:
    def __init__(self):
        self.tables = []

    def append_tables(self, table):
        if not isinstance(table, Table):
            raise ValueError("only support instance Table")
        self.tables.append(table)

    def to_string(self, indent):
        worker_str = "{}downpour_worker_param {{{}\n{}}}"
        table_strs = ""
        indent += 2
        for table in self.tables:
            table_strs += "\n"
            table_strs += table.to_string(indent)

        return worker_str.format(
            conv_indent(indent), table_strs, conv_indent(indent)
        )


class Worker:
    def __init__(self):
        self.workers = []

    def add_worker(self, worker):
        if not isinstance(worker, DownpourWorker):
            raise ValueError("only support instance DownpourWorker")
        self.workers.append(worker)

    def __str__(self):
        worker_str = "worker_param {{{}\n}}"
        indent = 2
        workers_str = ""
        for worker in self.workers:
            workers_str += "\n"
            workers_str += worker.to_string(indent)

        return worker_str.format(workers_str)


class fsClient:
    def __init__(self, proto):
        self.proto = proto
        self.uri = proto.uri
        self.user = proto.user
        self.passwd = proto.passwd
        self.hadoop_bin = proto.hadoop_bin

    def to_string(self):
        from google.protobuf import text_format

        proto_txt = text_format.MessageToString(self.proto)
        if proto_txt:
            fs_str = "fs_client_param {{\n{}}}"
            return fs_str.format(proto_txt)
        else:
            return ""


class TheOnePSRuntime(RuntimeBase):
    def __init__(self):
        super().__init__()
        self._communicator = None
        self._server = None
        self._worker = fluid.core.DistFleetWrapper()
        self._server_sub_program = []
        self._heter_client = None

    def _set_basic_info(self, context):
        self.context = context
        self.role_maker = context["role_maker"]
        self.origin_main_program = context["origin_main_program"]
        self.origin_startup_program = context["origin_startup_program"]
        self.async_strategy = self._get_distributed_strategy()
        self.compiled_strategy = self.build_compiled_startegy()

    def _get_distributed_strategy(self):
        strategy = None

        from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
            StrategyFactory,
        )

        dist_strategy = self.context["valid_strategy"]
        k_steps = dist_strategy.a_sync_configs["k_steps"]

        if not dist_strategy.a_sync and k_steps == 0:
            strategy = StrategyFactory.create_sync_strategy()

        if dist_strategy.a_sync and k_steps == 0:
            strategy = StrategyFactory.create_async_strategy()

        if dist_strategy.a_sync and k_steps > 0:
            strategy = StrategyFactory.create_geo_strategy(k_steps)

        if not strategy:
            raise ValueError("k_steps must be invalid value, please check")

        if dist_strategy.a_sync_configs["use_ps_gpu"]:
            strategy.use_ps_gpu = True
        return strategy

    def build_compiled_startegy(self):
        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            CompileTimeStrategy,
        )

        compiled_config = CompileTimeStrategy(
            self.origin_main_program,
            self.origin_main_program,
            self.async_strategy,
            self.role_maker,
        )
        if self.async_strategy.use_ps_gpu:
            compiled_config.use_ps_gpu = True
        return compiled_config

    def _init_worker(self):
        from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
            SyncStrategy,
        )

        is_sync = self.compiled_strategy.is_sync_mode()
        worker = self._get_fleet_proto(is_server=False, is_sync=is_sync)
        server = self._get_fleet_proto(is_server=True, is_sync=is_sync)

        dist_strategy = self.context["valid_strategy"]
        use_ps_gpu = dist_strategy.a_sync_configs["use_ps_gpu"]
        if use_ps_gpu:
            main_program = self.context['loss'].block.program
            if not main_program._fleet_opt:
                main_program._fleet_opt = {}
            main_program._fleet_opt["use_ps_gpu"] = True
            gpus_env = os.getenv("FLAGS_selected_gpus")
            main_program._fleet_opt["worker_places"] = [
                int(s) for s in gpus_env.split(",")
            ]

        def sync_strategy_envs():
            kwargs = {}
            kwargs[
                "pserver_endpoints"
            ] = self.role_maker._get_pserver_endpoints()
            kwargs["trainer_id"] = self.role_maker._worker_index()
            return kwargs

        proto_txt = str(worker) + "\n" + str(server)
        with open('proto_txt', 'w') as f:
            f.write(proto_txt)

        debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))

        if debug:
            print(f"worker: \n{proto_txt}")

        endpoints = self.compiled_strategy.get_ps_endpoints()

        string_hosts = []
        for idx, ep in enumerate(endpoints):
            host, port = ep.split(":")
            pshost = fluid.core.PSHost(host, int(port), idx)
            string_hosts.append(pshost.serialize_to_string())

        dense_map = self.compiled_strategy.get_the_one_recv_context(
            split_dense_table=self.role_maker._is_heter_parameter_server_mode
        )
        send_ctx = self.compiled_strategy.get_the_one_send_context(
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=self.role_maker._is_heter_parameter_server_mode,
            ep_list=endpoints,
        )
        trainer_config = self.async_strategy.get_trainer_runtime_config()

        debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))
        if debug:
            print(f"worker: \n{proto_txt}")
            print("communicator send_ctx:")
            for key in send_ctx:
                print(f"{key}: {send_ctx[key]}")
            for key in dense_map:
                print(f"{key}: {dense_map[key]}")

        kwargs = {}
        kwargs['need_global_step'] = "0"
        kwargs["trainer_id"] = self.role_maker._role_id()
        kwargs["trainers"] = self.role_maker._worker_num()
        # if self.role_maker._is_heter_worker():
        #    kwargs["trainer_id"] += kwargs["trainers"]

        for table in server.servers[0].tables:
            if table.table_class == "BarrierTable":
                kwargs["barrier_table_id"] = table.id
                break

        if isinstance(self.async_strategy, SyncStrategy):
            sync_kwargs = sync_strategy_envs()
            kwargs.update(sync_kwargs)

        from paddle.distributed.communicator import Communicator, HeterClient

        self._communicator = Communicator(
            trainer_config.mode, kwargs, trainer_config.get_communicator_flags()
        )
        self._communicator.init_with_ctx(
            send_ctx, dense_map, proto_txt, string_hosts, fluid.global_scope()
        )

        from paddle.distributed import fleet

        fleet.util.barrier()
        info = self._communicator.get_client_info()
        if isinstance(info, list) and len(info) > 0:
            all_info = self.role_maker._all_gather(info[0])
            # for unittest
            if not isinstance(all_info, list):
                warnings.warn("gloo may not initialize correctly")
                all_info = [all_info]
            self._communicator.set_clients(all_info)
            # create_c2c_connection default param:
            #  pserver_timeout_ms=500000
            #  pserver_connect_timeout_ms=10000
            #  max_retry=3
            self._communicator.create_client_to_client_connection()
            print('create c2c connection done')
        else:
            print('cannot create c2c connection')

        dist_strategy = self.context["valid_strategy"]

        is_test = bool(int(os.getenv("TEST_MODE", "0")))

        if (
            self.role_maker._is_first_worker()
            and self.role_maker._is_heter_parameter_server_mode
        ):
            # for ps-heter mode load all parameters on first_worker
            init_params = self.compiled_strategy.get_the_one_recv_context(
                split_dense_table=True, use_origin_program=True
            )
        else:
            init_params = dense_map

        if not is_test:
            self._communicator.init_params(init_params)
            fleet.util.barrier()
        self._communicator.pull_dense(init_params)
        fleet.util.barrier()

        if not self._communicator.is_running():
            self._communicator.start()
        else:
            warnings.warn("communicator has been initialized, skip")

        launch_barrier = dist_strategy.a_sync_configs["launch_barrier"]
        launch_barrier_flag = int(os.getenv("FLAGS_LAUNCH_BARRIER", "1"))
        if launch_barrier and launch_barrier_flag:
            # for trainer wait server ready
            wait_server_ready(self.role_maker._get_pserver_endpoints())
            if (
                self.role_maker._is_heter_parameter_server_mode
                and self.role_maker._get_next_trainers() != []
            ):
                wait_server_ready(self.role_maker._get_next_trainers())
            if self.role_maker._is_heter_parameter_server_mode:
                previous_trainers = []
                if self.role_maker._get_previous_trainers() != []:
                    previous_trainers = self.role_maker._get_previous_trainers()
                next_trainers = []
                if self.role_maker._get_next_trainers() != []:
                    next_trainers = self.role_maker._get_next_trainers()
                self._heter_client = HeterClient(
                    next_trainers, previous_trainers, self.role_maker._role_id()
                )

    def _push_sparse_param(
        self, var_name, table_id=-1, scope=fluid.global_scope()
    ):
        self._communicator.push_sparse_param(var_name, table_id, scope)

    def _get_executor(self):
        executor = fluid.Executor(fluid.CPUPlace())
        if self.role_maker._is_heter_parameter_server_mode:
            if self.role_maker._is_heter_worker():
                heter_device_type = self.role_maker._heter_device_type().upper()
                if heter_device_type not in ["GPU", "XPU", "CPU"]:
                    raise ValueError(
                        "Heter Worker Not Support Device {}".format(
                            heter_device_type
                        )
                    )
                if heter_device_type == "GPU":
                    executor = Executor(
                        fluid.CUDAPlace(
                            int(os.getenv("FLAGS_selected_gpus", "0"))
                        )
                    )
                elif heter_device_type == "XPU":
                    executor = Executor(
                        fluid.XPUPlace(
                            int(os.getenv("FLAGS_selected_xpus", "0"))
                        )
                    )
        return executor

    def _get_fleet_proto(self, is_server, is_sync, **kwargs):
        def _build_merge_accessor(ctx):
            accessor = Accessor()
            accessor.accessor_class = "CommMergeAccessor"
            accessor.optimizer = None

            if ctx.is_sparse():
                accessor.feature_dim = ctx.sections()[0]
                accessor.embedding_dim = ctx.sections()[1]
            else:
                accessor.feature_dim = ctx.sections()[0]
                accessor.embedding_dim = 1

            return accessor

        def _build_barrier_table(idx):
            table = Table()
            table.id = idx
            table.type = "PS_OTHER_TABLE"
            table.table_class = "BarrierTable"
            table.shard_num = 256

            accessor = Accessor()
            accessor.accessor_class = "CommMergeAccessor"
            accessor.optimizer = None
            accessor.feature_dim = 0
            accessor.embedding_dim = 0
            table.accessor = accessor

            common = CommonAccessor()
            common.table_name = "barrier_table"
            trainer_num = self.compiled_strategy.get_trainers()
            if self.role_maker._is_heter_parameter_server_mode:
                trainer_num += len(
                    self.role_maker._get_heter_worker_endpoints()
                )
            common.trainer_num = trainer_num
            common.attrs = ""
            common.dims = []
            common.params = []
            table.common = common
            return table

        def _build_tensor_table(idx, tensor_dict):
            table = Table()
            table.id = idx
            table.type = "PS_OTHER_TABLE"
            table.table_class = tensor_dict["tensor_table_class"]
            table.shard_num = 256

            accessor = Accessor()
            accessor.accessor_class = "CommMergeAccessor"
            accessor.optimizer = None
            accessor.feature_dim = 0
            accessor.embedding_dim = 0
            table.accessor = accessor

            common = CommonAccessor()
            common.table_name = tensor_dict["feed_var_name"]
            common.trainer_num = self.compiled_strategy.get_trainers()
            common.attrs = ""
            common.dims = []
            common.params = []
            table.common = common

            tensor = Tensor()
            tensor.main_program_id = tensor_dict["main_program_id"]
            tensor.startup_program_id = tensor_dict["startup_program_id"]
            tensor.feed_var_name = tensor_dict["feed_var_name"]
            tensor.fetch_var_name = tensor_dict["fetch_var_name"]
            tensor.tensor_table_class = tensor_dict["tensor_table_class"]
            table.tensor = tensor

            return table

        def _add_tensor_table(tables):
            tensor_table_dict = self.compiled_strategy.get_tensor_table_dict()
            program_idx = 0
            for table_name in tensor_table_dict:
                if tensor_table_dict[table_name]["startup_program"] is not None:
                    tensor_table_dict[table_name][
                        "startup_program_id"
                    ] = program_idx
                    self._server_sub_program.append(
                        tensor_table_dict[table_name]["startup_program"].desc
                    )
                    program_idx += 1
                if tensor_table_dict[table_name]["main_program"] is not None:
                    tensor_table_dict[table_name][
                        "main_program_id"
                    ] = program_idx
                    self._server_sub_program.append(
                        tensor_table_dict[table_name]["main_program"].desc
                    )
                    program_idx += 1
                # Todo: Hard code for lr_decay table apply table id
                new_table = _build_tensor_table(
                    len(tables), tensor_table_dict[table_name]
                )
                tables.append(new_table)
            return tables

        def _get_tables():
            send_ctx = self.compiled_strategy.get_the_one_send_context(
                use_origin_program=True,
                split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            )

            tables = []
            for idx, (name, ctx) in enumerate(send_ctx.items()):
                if ctx.is_tensor_table() or len(ctx.origin_varnames()) < 1:
                    continue

                table = Table()
                table.id = ctx.table_id()
                common = CommonAccessor()

                if ctx.is_sparse():
                    table.type = "PS_SPARSE_TABLE"
                    table.shard_num = 256

                    common.table_name = (
                        self.compiled_strategy.grad_name_to_param_name[
                            ctx.origin_varnames()[0]
                        ]
                    )

                    if self.compiled_strategy.is_geo_mode():
                        table.table_class = "MemorySparseGeoTable"
                    else:
                        all_table_proto = self.context[
                            "user_defined_strategy"
                        ].sparse_table_configs
                        table_proto = all_table_proto.add()
                        for proto in all_table_proto:
                            if proto.table_name == common.table_name:
                                table_proto = proto
                                break
                        if table_proto.HasField("table_class"):
                            table.table_class = table_proto.table_class
                        else:
                            table.table_class = parse_table_class(
                                common.table_name, self.origin_main_program
                            )
                        if table.table_class != 'MemorySparseTable':
                            table.table_class = 'MemorySparseTable'
                            warnings.warn(
                                "The PS mode must use MemorySparseTable."
                            )

                        if table_proto.HasField("shard_num"):
                            table.shard_num = table_proto.shard_num
                        else:
                            table.shard_num = 1000
                            warnings.warn(
                                "The shard_num of sparse table is not set, use default value 1000."
                            )

                        if table_proto.accessor.ByteSize() == 0:
                            warnings.warn(
                                "The accessor of sparse table is not set, use default value."
                            )
                        get_default_accessor_proto(
                            table_proto.accessor,
                            common.table_name,
                            self.origin_main_program,
                        )
                        check_embedding_dim(
                            table_proto.accessor,
                            common.table_name,
                            self.origin_main_program,
                        )
                        from google.protobuf import text_format

                        table.accessor_proto = text_format.MessageToString(
                            table_proto.accessor
                        )
                else:
                    table.type = "PS_DENSE_TABLE"
                    table.table_class = "MemoryDenseTable"
                    table.shard_num = 256
                    common.table_name = "MergedDense"

                adam_d2sum = self.context["user_defined_strategy"].adam_d2sum
                common.parse_by_optimizer(
                    ctx.origin_varnames()[0],
                    ctx.is_sparse(),
                    ctx.sections()[0],
                    ctx.sections()[1] if ctx.is_sparse() else 1,
                    self.compiled_strategy,
                    adam_d2sum,
                )

                if ctx.is_sparse():
                    common.parse_entry(
                        common.table_name, self.origin_main_program
                    )

                if is_sync:
                    common.sync = "true"
                else:
                    common.sync = "false"
                table.common = common

                if table.table_class != 'MemorySparseTable':
                    accessor = _build_merge_accessor(ctx)
                    table.accessor = accessor
                tables.append(table)

            tensor_table_dict = self.compiled_strategy.get_tensor_table_dict()
            if len(tensor_table_dict) > 0:
                tables = _add_tensor_table(tables)
            else:
                empty_porgram = Program()
                self._server_sub_program.append(empty_porgram.desc)

            barrier_table = _build_barrier_table(len(tables))
            tables.append(barrier_table)
            return tables

        if is_server:
            server = Server()
            downpour_server = DownpourServer()

            service = Service()
            dist_strategy = self.context["valid_strategy"]
            use_ps_gpu = dist_strategy.a_sync_configs["use_ps_gpu"]
            if use_ps_gpu:
                service.server_class = "PsLocalServer"
                service.client_class = "PsLocalClient"
            downpour_server.set_service_param(service)

            tables = _get_tables()
            downpour_server.tables = tables
            server.add_server(downpour_server)
            return server
        else:
            worker = Worker()
            downpour_worker = DownpourWorker()

            tables = _get_tables()
            downpour_worker.tables = tables
            worker.add_worker(downpour_worker)
            return worker

    def _init_server(self, dirname=None, var_names=None, **kwargs):
        role_id = self.compiled_strategy.get_role_id()
        endpoints = self.compiled_strategy.get_ps_endpoints()
        is_sync = self.compiled_strategy.is_sync_mode()
        trainers = self.compiled_strategy.get_trainers()
        if self.role_maker._is_heter_parameter_server_mode:
            trainers += len(self.role_maker._get_heter_worker_endpoints())
        server = self._get_fleet_proto(is_server=True, is_sync=is_sync)
        proto_txt = str(server)
        fs_client = fsClient(
            self.context["user_defined_strategy"].fs_client_param
        )
        proto_txt = proto_txt + "\n" + fs_client.to_string()

        debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))
        if debug:
            print(f"server: \n{proto_txt}")

        string_hosts = []
        for idx, ep in enumerate(endpoints):
            host, port = ep.split(":")
            pshost = fluid.core.PSHost(host, int(port), idx)
            string_hosts.append(pshost.serialize_to_string())

        self._server = fluid.core.DistFleetWrapper()
        self._server.init_server(
            proto_txt, string_hosts, role_id, trainers, self._server_sub_program
        )

        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            get_sparse_tablenames,
        )

        dist_varnames = get_sparse_tablenames(self.origin_main_program, True)
        sparse_varnames = get_sparse_tablenames(self.origin_main_program, False)

        distributed_varnames = dist_varnames + sparse_varnames

        if var_names is None:
            load_varnames = distributed_varnames
        else:
            for var_name in var_names:
                if var_name not in distributed_varnames:
                    raise ValueError(
                        "fleet.init server can only load sparse variables in {}".format(
                            distributed_varnames
                        )
                    )
            load_varnames = var_names

        if dirname is None or not load_varnames:
            return

        sparse_table_maps = {}
        for table in server.servers[0].tables:
            if table.type == "PS_SPARSE_TABLE" and table.common is not None:
                sparse_table_maps[table.common.table_name] = table.id

        dirname = os.path.normpath(dirname)
        pserver_id = self.role_maker._role_id()

        for var_name in load_varnames:
            table_id = sparse_table_maps[var_name]
            # path = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
            #                     "{}.block{}.txt".format(var_name, pserver_id))
            # meta = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
            #                     "{}.block{}.meta".format(var_name, pserver_id))
            self._server.load_sparse(dirname, "0", table_id)

    def _run_server(self):
        ep = self.compiled_strategy.get_ps_endpoint()
        host, port = ep.split(":")
        self._server.run_server(host, int(port))

    def _stop_worker(self):
        self._communicator.stop()
        if self.role_maker._is_heter_parameter_server_mode:
            assert (
                self._heter_client is not None
            ), "heter client should not be None in heterps mode"
            self._heter_client.stop()
        # executor = self._get_executor()
        # executor.close()

    @staticmethod
    def __exclude_vars(exclude_var_names=[]):
        def is_valid(var):
            if var.name in exclude_var_names:
                return False

            from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
                _get_varname_parts,
            )

            origin_varname, _, _ = _get_varname_parts(var.name)
            if origin_varname.endswith("@GRAD"):
                return False

            if origin_varname == "learning_rate_0":
                return False

            if (
                var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH
                or var.desc.type() == core.VarDesc.VarType.FETCH_LIST
                or var.desc.type() == core.VarDesc.VarType.READER
            ):
                return False
            return var.persistable

        return is_valid

    def _get_inference_model_path(self, dirname):
        if dirname.startswith("afs:") or dirname.startswith("hdfs:"):
            model_path = "./dnn_plugin"
        else:
            model_path = os.path.join(dirname, "dnn_plugin")
        return model_path

    def _save_sparse_params(
        self, executor, dirname, context, main_program, mode
    ):
        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            get_sparse_tablenames,
        )

        distributed_varnames = get_sparse_tablenames(
            self.compiled_strategy.origin_main_program, True
        )
        values = []
        model_path = self._get_inference_model_path(dirname)
        for id, names in context.items():
            if names[0] not in distributed_varnames:
                # only save sparse param to local
                try:
                    self._worker.recv_and_save_model(id, model_path)
                except:
                    pass
            # save sparse & distributed param on server
            self._worker.save_one_model(id, dirname, mode)
            values.extend(names)
        # self._worker.save_all_model(dirname, mode)
        return values

    def _save_distributed_persistables(
        self, executor, dirname, main_program, mode=0
    ):
        denses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=True,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )
        sparses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=False,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )

        sparse_varnames = self._save_sparse_params(
            executor, dirname, sparses, main_program, mode
        )

        recv_dense_varnames = []
        for id, names in denses.items():
            recv_dense_varnames.extend(names)
        self._communicator.pull_dense(denses)

        saved_varnames = sparse_varnames

        remaining_vars = list(
            filter(
                TheOnePSRuntime.__exclude_vars(saved_varnames),
                main_program.list_vars(),
            )
        )

        import paddle

        for var in remaining_vars:
            # if var.name not in recv_dense_varnames:
            #     continue
            tensor = var.get_value()
            paddle.save(
                tensor, os.path.join(dirname, var.name), use_binary_format=True
            )

    def _ps_inference_save_persistables(
        self, executor, dirname, main_program=None, mode=0, **kwargs
    ):
        """
        This function filters out all variables with `persistable==True` from the
        give `main_program` and then saves these variables to the folder `dirname`
        or file `filename`.

        The `dirname` is used to specify the folder where persistable variables
        are going to be saved. If you would like to save variables in separate
        files, set `filename` None; if you would like to save all variables in a
        single file, use `filename` to specify the file name.
        """

        if not isinstance(executor, Executor):
            raise TypeError(
                "in fleet.save() function, executor must be as Executor type"
            )

        if main_program is None:
            main_program = self.compiled_strategy.get_origin_ps_main_program()

        if isinstance(main_program, CompiledProgram):
            raise TypeError(
                "in fleet.save() function, main_program must be as Program type, CompiledProgram is not allowed"
            )

        # Todo(MrChengmo): Save optimizer status
        # self._save_distributed_persistables(executor, dirname, main_program,
        #                                     mode)
        self._worker.save_all_model(dirname, mode)

    def _ps_inference_save_inference_model(
        self,
        executor,
        dirname,
        feeded_var_names,
        target_vars,
        main_program=None,
        export_for_deployment=True,
        mode=0,
    ):
        """
        Prune the given `main_program` to build a new program especially for inference,
        and then save it and all related parameters to given `dirname` by the `executor`.
        """

        if not isinstance(executor, Executor):
            raise TypeError(
                "in fleet.save() function, executor must be as Executor type"
            )

        import paddle

        program = (
            self.origin_main_program if main_program is None else main_program
        )

        if isinstance(program, CompiledProgram):
            raise TypeError(
                "in fleet.save() function, main_program must be as Program type, CompiledProgram is not allowed"
            )

        feed_vars = [
            program.global_block().var(name) for name in feeded_var_names
        ]

        infer_program = paddle.static.normalize_program(
            program, feed_vars, target_vars
        )

        infer_program._copy_dist_param_info_from(program)

        model_path = self._get_inference_model_path(dirname)
        model_basename = "__model__"
        model_basename = os.path.join(model_path, model_basename)
        paddle.save(infer_program, model_basename)

        sparses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=False,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )
        sparse_names = self._save_sparse_params(
            executor, dirname, sparses, main_program, mode
        )

        denses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=True,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )
        # TODO(zhaocaibei123): for GEO: should call GeoCommunicator::RecvDense
        self._communicator.pull_dense(denses)

        generate_vars = self.context[
            "user_defined_strategy"
        ].trainer_desc_configs["stat_var_names"]
        generate_vars = list(generate_vars)
        remaining_vars = list(
            filter(
                TheOnePSRuntime.__exclude_vars(sparse_names),
                infer_program.list_vars(),
            )
        )

        for var in remaining_vars:
            tensor = var.get_value()
            paddle.save(
                tensor,
                os.path.join(model_path, var.name),
                use_binary_format=True,
            )

    def _save_inference_model(self, *args, **kwargs):
        self._ps_inference_save_inference_model(*args, **kwargs)

    def _save_persistables(self, *args, **kwargs):
        self._ps_inference_save_persistables(*args, **kwargs)

    def _load_sparse_params(self, dirname, context, main_program, mode):
        from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
            get_sparse_tablenames,
        )

        distributed_varnames = get_sparse_tablenames(
            self.compiled_strategy.origin_main_program, True
        )
        values = []
        for id, names in context.items():
            if names[0] not in distributed_varnames:
                # TODO: only load sparse param from local
                warnings.warn("varname is not in distributed_varnames, pass")
            # load sparse & distributed param on server
            self._worker.load_one_table(id, dirname, mode)
            values.extend(names)
        return values

    def _ps_inference_load_inference_model(
        self, dirname, mode=0, main_program=None
    ):
        if main_program is None:
            main_program = self.compiled_strategy.get_origin_ps_main_program()

        if isinstance(main_program, CompiledProgram):
            raise TypeError(
                "in fleet.save() function, main_program must be as Program type, CompiledProgram is not allowed"
            )

        denses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=True,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )
        sparses = self.compiled_strategy.get_the_one_recv_context(
            is_dense=False,
            split_dense_table=self.role_maker._is_heter_parameter_server_mode,
            use_origin_program=True,
        )

        sparse_varnames = self._load_sparse_params(
            dirname, sparses, main_program, mode
        )

        recv_dense_varnames = []
        for id, names in denses.items():
            recv_dense_varnames.extend(names)

        loaded_varnames = sparse_varnames

        remaining_vars = list(
            filter(
                TheOnePSRuntime.__exclude_vars(loaded_varnames),
                main_program.list_vars(),
            )
        )

        if dirname.startswith("afs:") or dirname.startswith("hdfs:"):
            model_path = "./dnn_plugin"
        else:
            model_path = os.path.join(dirname, "dnn_plugin")
        import paddle

        for var in remaining_vars:
            if var.name not in recv_dense_varnames:
                continue
            tensor = paddle.load(os.path.join(model_path, var.name))
            var.set_value(tensor)

        self._communicator.init_params(denses)

    def _load_distributed_persistables(self, path, mode):
        self._worker.load_model(path, mode)

    def load_model(self, path, mode):
        if mode == 0 or mode == 3:
            self._load_distributed_persistables(path, mode)
        else:
            self._ps_inference_load_inference_model(path, mode)
        # self._load_distributed_persistables(path, mode=mode)

    def _shrink(self, threshold=None):
        if threshold is not None:
            warnings.warn(
                "The param threshold is not used in MemorySparseTable, if you need to shrink, please set the config of accessor"
            )
        else:
            threshold = 0
        from paddle.distributed import fleet

        fleet.util.barrier()
        if self.role_maker._is_first_worker():
            sparses = self.compiled_strategy.get_the_one_recv_context(
                is_dense=False,
                split_dense_table=self.role_maker._is_heter_parameter_server_mode,
                use_origin_program=True,
            )

            for id, names in sparses.items():
                self._worker.shrink_sparse_table(id, threshold)
        fleet.util.barrier()
