#   Copyright (c) 2018 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.
"""
Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename split grad variables to add trainer_id suffix ".trainer_%d".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send split variables to server and
5. add recv_op to fetch params(split blocks or origin param) from server.
6. append concat_op to merge split blocks to update local weights.

Steps to transpile pserver:
1. create new program for parameter server.
2. create params and grad variables that assigned to current server instance.
3. create a sub-block in the server side program
4. append ops that should run on current server instance.
5. add listen_and_serv op
"""

import collections
import logging
import math
import os
import sys
from functools import reduce

import numpy as np

from paddle import framework
from paddle.base.framework import grad_var_name
from paddle.framework import Block, Program, core
from paddle.incubate.distributed.fleet.parameter_server.ir.ps_dispatcher import (
    PSDispatcher,
    RoundRobin,
)
from paddle.nn.initializer import Constant
from paddle.static import (
    Parameter,
    default_main_program,
    default_startup_program,
)
from paddle.utils import unique_name

LOOKUP_TABLE_TYPE = ["lookup_table", "lookup_table_v2"]
LOOKUP_TABLE_GRAD_TYPE = ["lookup_table_grad", "lookup_table_v2_grad"]
OP_NAME_SCOPE = "op_namescope"
CLIP_OP_NAME_SCOPE = "@CLIP"
OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = (
    core.op_proto_and_checker_maker.kOpRoleAttrName()
)
OPT_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Optimize
RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
DIST_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Dist
LR_SCHED_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.LRSched

PRINT_LOG = False


class DistributedMode:
    SYNC = 0
    ASYNC = 1
    HALF_ASYNC = 2
    GEO = 3


def log(*args):
    if PRINT_LOG:
        print(args)


class VarBlock:
    def __init__(self, varname, offset, size):
        self.varname = varname
        # NOTE: real offset is offset * size
        self.offset = offset
        self.size = size

    def __str__(self):
        return f"{self.varname}:{self.offset}:{self.size}"


def same_or_split_var(p_name, var_name):
    return p_name == var_name or p_name.startswith(var_name + ".block")


def slice_variable(var_list, slice_count, min_block_size):
    """
    We may need to split dense tensor to one or more blocks and put
    them equally onto parameter server. One block is a sub-tensor
    aligned by dim[0] of the tensor.

    We need to have a minimal block size so that the calculations in
    the parameter server side can gain better performance. By default
    minimum block size 8K elements (maybe 16bit or 32bit or 64bit).

    Args:
        var_list (list): List of variables.
        slice_count (int): Numel of count that variables will be sliced, which
            could be the pserver services' count.
        min_block_size (int): Minimum split block size.
    Returns:
        blocks (list[(varname, block_id, current_block_size)]): A list
            of VarBlocks. Each VarBlock specifies a shard of the var.
    """
    blocks = []
    for var in var_list:
        split_count = slice_count
        var_numel = reduce(lambda x, y: x * y, var.shape, 1)
        max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
        if max_pserver_count == 0:
            max_pserver_count = 1
        if max_pserver_count < slice_count:
            split_count = max_pserver_count
        block_size = int(math.ceil(var_numel / float(split_count)))

        if len(var.shape) >= 2:
            # align by dim1(width)
            dim1 = reduce(lambda x, y: x * y, var.shape[1:], 1)
            remains = block_size % dim1
            if remains != 0:
                block_size += dim1 - remains
        # update split_count after aligning
        split_count = int(math.ceil(var_numel / float(block_size)))
        for block_id in range(split_count):
            curr_block_size = min(
                block_size, var_numel - ((block_id) * block_size)
            )
            block = VarBlock(var.name, block_id, curr_block_size)
            blocks.append(str(block))
    return blocks


class DistributeTranspilerConfig:
    """
        :api_attr: Static Graph

    A configuration class that provide support for transpiler distributed jobs.
    Some important parameters are explained as follows:


    .. py:attribute:: slice_var_up (bool)

          Whether to do Tensor slice for parameter servers, default is True.

    .. py:attribute:: split_method (PSDispatcher)

          Methods of dispatching parameters for server,
          `RoundRobin` or
          `HashName` (both from `paddle.incubate.distributed.fleet.parameter_server.ir.ps_dispatcher`) can be used and default is RoundRobin.
          Try to choose the best method to balance loads for parameter servers.

    .. py:attribute:: min_block_size (int)

          Minimum number of split elements in block, default is 8192.

          According to : https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
          We can use bandwidth efficiently when data size is larger than 2MB.If you
          want to change it, please be sure you have read the slice_variable function. You can find
          the definition of slice_variable in
          https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/transpiler/distribute_transpiler.py
          .

    Examples:
        .. code-block:: python

            >>> from paddle.distributed.transpiler.distribute_transpiler import RoundRobin
            >>> import paddle.distributed.transpiler as transpiler

            >>> config = transpiler.DistributeTranspilerConfig()
            >>> config.slice_var_up = True
            >>> config.split_method = RoundRobin
            >>> config.min_block_size = 81920

    """

    slice_var_up = True
    split_method = None
    min_block_size = 8192
    enable_dc_asgd = False
    # supported modes: pserver, nccl2, collective
    mode = "pserver"
    print_log = False
    wait_port = True
    # split the send recv var in runtime
    __runtime_split_send_recv = False
    __sync_mode = True

    # half_async
    half_async = False
    completely_not_async = False

    # Geo-sgd algorithm
    geo_sgd_mode = False
    geo_sgd_need_push_nums = 100

    nccl_comm_num = 1
    # The picture here illustrates the principle:
    # https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
    use_hierarchical_allreduce = False
    # Nccl ranks in a node when use hierarchical allreduce, it's set to gpu cards' number in most cases.
    hierarchical_allreduce_inter_nranks = 0

    # if mode is collective
    # supported modes: grad_allreduce, local_sgd
    collective_mode = None

    def __init__(self):
        pass

    @property
    def runtime_split_send_recv(self):
        return self.__runtime_split_send_recv

    @runtime_split_send_recv.setter
    def runtime_split_send_recv(self, value):
        if value is None:
            raise ValueError("runtime_split_send_recv can't be None")
        if value and self.__sync_mode:
            raise ValueError(
                "if you want to set runtime_split_send_recv to be true, make ensure config.sync_mode is false at first"
            )
        self.__runtime_split_send_recv = value

    @property
    def sync_mode(self):
        return self.__sync_mode

    @sync_mode.setter
    def sync_mode(self, value):
        if value is None:
            raise ValueError("sync_mode can't be None")
        if value and self.__runtime_split_send_recv:
            raise ValueError(
                "if you want to set sync_mode to be true, make ensure config.runtime_split_send_recv is false at first"
            )
        self.__sync_mode = value


class ServerRuntimeConfig:
    def __init__(self):
        self._rpc_send_thread_num = int(
            os.getenv("FLAGS_rpc_send_thread_num", "12")
        )
        self._rpc_get_thread_num = int(
            os.getenv("FLAGS_rpc_get_thread_num", "12")
        )
        self._rpc_prefetch_thread_num = int(
            os.getenv("FLAGS_rpc_prefetch_thread_num", "12")
        )


class DistributeTranspiler:
    """
        :api_attr: Static Graph

    **DistributeTranspiler**

    Convert the base program to distributed data-parallelism programs.
    Supports two modes: parameter server(pserver) mode and nccl2 mode.

    In pserver mode, the main_program will be transformed to use a remote
    parameter server to do parameter optimization. And the optimization
    graph will be put into a parameter server program.

    In nccl2 mode, the transpiler will append a NCCL_ID broadcasting
    op in startup_program to share the NCCL_ID across the job nodes.
    After transpile_nccl2 called, you ***must*** pass trainer_id and
    num_trainers argument to ParallelExecutor to enable NCCL2 distributed
    mode.

    Examples:
        .. code-block:: python

            >>> # doctest: +REQUIRES(env:DISTRIBUTED)
            >>> import paddle
            >>> import paddle.base as base
            >>> import paddle.distributed.transpiler as transpiler

            >>> paddle.enable_static()

            >>> x = paddle.static.data(name='x', shape=[1,13], dtype='float32')
            >>> y = paddle.static.data(name='y', shape=[1], dtype='float32')
            >>> y_predict = paddle.static.nn.fc(x, size=1, activation=None)

            >>> cost = paddle.nn.functional.square_error_cost(input=y_predict, label=y)
            >>> avg_loss = paddle.mean(cost)

            >>> sgd_optimizer = paddle.optimizer.SGD(learning_rate=0.001)
            >>> sgd_optimizer.minimize(avg_loss)

            >>> # for pserver mode
            >>> pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
            >>> trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
            >>> current_endpoint = "192.168.0.1:6174"
            >>> trainer_id = 0
            >>> trainers = 4
            >>> role = "PSERVER"

            >>> t = transpiler.DistributeTranspiler()
            >>> t.transpile(
            ...         trainer_id, pservers=pserver_endpoints, trainers=trainers)

            >>> if role == "PSERVER":
            ...         pserver_program = t.get_pserver_program(current_endpoint)
            ...         pserver_startup_program = t.get_startup_program(current_endpoint,
            ...                                                     pserver_program)
            ... elif role == "TRAINER":
            ...         trainer_program = t.get_trainer_program()

            >>> # for nccl2 mode
            >>> trainer_num = 2
            >>> trainer_id = 0
            >>> config = transpiler.DistributeTranspilerConfig()
            >>> config.mode = "nccl2"
            >>> trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
            >>> t = transpiler.DistributeTranspiler(config=config)
            >>> t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174")
            >>> exe = paddle.static.ParallelExecutor(
            ...     use_cuda=True,
            ...     loss_name=avg_loss.name,
            ...     num_trainers=trainer_num,
            ...     trainer_id=trainer_id
            ... )

    """

    def __init__(self, config=None):
        if config is not None:
            self.config = config
        else:
            self.config = DistributeTranspilerConfig()
        self._set_server_config()

        if self.config.split_method is None:
            self.config.split_method = RoundRobin

        if self.config.sync_mode or self.config.completely_not_async:
            self.distributed_mode = DistributedMode.SYNC
        elif self.config.runtime_split_send_recv:
            self.distributed_mode = DistributedMode.ASYNC
        else:
            self.distributed_mode = DistributedMode.HALF_ASYNC

        global PRINT_LOG
        if self.config.print_log:
            PRINT_LOG = True
        assert self.config.min_block_size >= 8192
        assert self.config.split_method.__bases__[0] == PSDispatcher
        self.counter_var = None

    def _set_server_config(self, server_config=None):
        if server_config is None:
            self.server_config = ServerRuntimeConfig()
        elif isinstance(server_config, ServerRuntimeConfig):
            self.server_config = server_config
        else:
            raise TypeError(
                "In DistributeTranspiler, server_config must be an instance of ServerRuntimeConfig"
            )

    def _transpile_nccl2(
        self,
        trainer_id,
        trainers,
        current_endpoint,
        startup_program=None,
        wait_port=True,
    ):
        from paddle.distributed.fleet.base.private_helper_function import (
            wait_server_ready,
        )

        if not startup_program:
            startup_program = default_startup_program()
        if trainer_id >= 0:
            worker_endpoints = trainers.split(",")
            # send NCCL_ID to others or recv from trainer 0
            worker_endpoints.remove(current_endpoint)
            if trainer_id == 0 and wait_port:
                wait_server_ready(worker_endpoints)

            nccl_id_var = startup_program.global_block().create_var(
                name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW
            )

            for i in range(1, self.config.nccl_comm_num):
                startup_program.global_block().create_var(
                    name=f"NCCLID_{i}",
                    persistable=True,
                    type=core.VarDesc.VarType.RAW,
                )

            if self.config.use_hierarchical_allreduce:
                for i in range(0, self.config.nccl_comm_num):
                    startup_program.global_block().create_var(
                        name=f"Hierarchical_inter_NCCLID_{i}",
                        persistable=True,
                        type=core.VarDesc.VarType.RAW,
                    )
                    startup_program.global_block().create_var(
                        name=f"Hierarchical_exter_NCCLID_{i}",
                        persistable=True,
                        type=core.VarDesc.VarType.RAW,
                    )

            startup_program.global_block().append_op(
                type="gen_nccl_id",
                inputs={},
                outputs={"NCCLID": nccl_id_var},
                attrs={
                    "trainers": trainers.split(","),
                    "trainer_id": trainer_id,
                    "nccl_comm_num": self.config.nccl_comm_num,
                    "use_hierarchical_allreduce": self.config.use_hierarchical_allreduce,
                    "hierarchical_allreduce_inter_nranks": self.config.hierarchical_allreduce_inter_nranks,
                },
            )
            return nccl_id_var
        else:
            raise ValueError("must set trainer_id > 0")

    def _transpile_collective(
        self,
        collective_mode,
        trainer_id,
        trainers,
        current_endpoint,
        startup_program=None,
        main_program=None,
        wait_port=True,
    ):
        from paddle.distributed.transpiler import collective

        if isinstance(trainers, str):
            endpoints = trainers.split(",")
        elif isinstance(trainers, list):
            endpoints = trainers
        elif collective_mode != "single_process_multi_thread":
            raise ValueError('invalid trainers config: ' + str(trainers))

        if (
            len(endpoints) == 1
            and collective_mode != "single_process_multi_thread"
        ):
            raise ValueError('invalid trainer number in distributed: 1')

        if startup_program is None:
            startup_program = default_startup_program()

        if main_program is None:
            main_program = default_main_program()

        transpiler = None
        if collective_mode == 'grad_allreduce':
            transpiler = collective.GradAllReduce(self.config.nccl_comm_num)
        elif collective_mode == 'local_sgd':
            transpiler = collective.LocalSGD(self.config.nccl_comm_num)
        elif collective_mode == "single_process_multi_thread":
            transpiler = collective.SingleProcessMultiThread()
        else:
            raise ValueError(f'invalid collective_mode: {collective_mode}')

        transpiler.transpile(
            startup_program=startup_program,
            main_program=main_program,
            rank=trainer_id,
            endpoints=endpoints,
            current_endpoint=current_endpoint,
            wait_port=wait_port,
        )

    def _get_all_remote_sparse_update_op(self, main_program):
        sparse_update_ops = []
        sparse_update_op_types = ["lookup_table", "nce", "lookup_table_v2"]
        for op in main_program.global_block().ops:
            if (
                op.type in sparse_update_op_types
                and op.attr('remote_prefetch') is True
            ):
                sparse_update_ops.append(op)
        return sparse_update_ops

    def _update_remote_sparse_update_op(
        self, program, need_sparse_update_params
    ):
        for param_varname, attrs in need_sparse_update_params.items():
            height_sections = self.sparse_param_to_height_sections[
                param_varname
            ]
            endpoints = attrs[0]
            table_names = attrs[1]

            ops = []
            op_type = ""
            used_ops = []

            for idx, op in enumerate(self.sparse_update_ops):
                if param_varname in op.input_arg_names and op_type == "":
                    op_type = op.type
                    ops.append(op)
                    used_ops.append(idx)

                elif param_varname in op.input_arg_names and op_type == op.type:
                    ops.append(op)
                    used_ops.append(idx)

            if op_type in LOOKUP_TABLE_TYPE:
                all_ops = program.global_block().ops
                op_idxs = [all_ops.index(op) for op in ops]
                inputs = [
                    program.global_block().vars[op.input("Ids")[0]]
                    for op in ops
                ]
                w = program.global_block().vars[ops[0].input("W")[0]]
                padding_idx = ops[0].attr("padding_idx")
                outputs = [
                    program.global_block().vars[op.output("Out")[0]]
                    for op in ops
                ]

                for idx in op_idxs[::-1]:
                    program.global_block()._remove_op(idx)

                inputs_idxs = [-1] * len(inputs)
                outputs_idxs = [-1] * len(outputs)

                for idx, op in enumerate(program.global_block().ops):
                    for i in range(0, len(op.output_names)):
                        outs = op.output(op.output_names[i])
                        for in_id, in_var in enumerate(inputs):
                            if in_var.name in outs:
                                inputs_idxs[in_id] = idx
                    for i in range(0, len(op.input_names)):
                        ins = op.input(op.input_names[i])
                        for out_id, out_var in enumerate(outputs):
                            if out_var.name in ins:
                                outputs_idxs[out_id] = idx

                if min(outputs_idxs) - max(inputs_idxs) >= 1:
                    distributed_idx = max(inputs_idxs) + 1

                    program.global_block()._insert_op(
                        index=distributed_idx,
                        type="distributed_lookup_table",
                        inputs={"Ids": inputs, 'W': w},
                        outputs={"Outputs": outputs},
                        attrs={
                            "table_names": table_names,
                            "height_sections": height_sections,
                            "endpoints": endpoints,
                            "padding_idx": padding_idx,
                            "trainer_id": self.trainer_id,
                            "lookup_table_version": op_type,
                        },
                    )
                else:
                    raise ValueError(
                        "something wrong with distribute_transpiler, submit a issue is recommended"
                    )

                for idx in used_ops[::-1]:
                    self.sparse_update_ops.pop(idx)

    def _is_input_of_remote_sparse_update_op(self, param_name):
        for op in self.sparse_update_ops:
            if param_name in op.input_arg_names:
                return True
        return False

    def transpile(
        self,
        trainer_id,
        program=None,
        pservers="127.0.0.1:6174",
        trainers=1,
        sync_mode=True,
        startup_program=None,
        current_endpoint="127.0.0.1:6174",
    ):
        """
        Transpile the input program to distributed programs with config and arguments.

        Args:
            trainer_id (int): id for current trainer worker, if you have
                n workers, the id may range from 0 ~ n-1
            program (Program|None): program to transpile,
                default is paddle.static.default_main_program().
            startup_program (Program|None): startup_program to transpile,
                default is paddle.static.default_startup_program().
            pservers (str): comma separated ip:port string for the pserver
                list.
            trainers (int|str): in pserver mode this is the number of
                trainers, in nccl2 mode this is a string of trainer
                endpoints.
            sync_mode (bool): Do sync training or not, default is True.
            startup_program (Program|None): startup_program to transpile,
                default is paddle.static.default_main_program().
            current_endpoint (str): need pass current endpoint when
                transpile as nccl2 distributed mode. In pserver mode
                this argument is not used.

        Examples:
            .. code-block:: python

                >>> # doctest: +REQUIRES(env:DISTRIBUTED)
                >>> t = paddle.distributed.transpiler.DistributeTranspiler()
                >>> t.transpile(
                ...     trainer_id=0,
                ...     pservers="127.0.0.1:7000,127.0.0.1:7001",
                ...     trainers=2,
                ...     sync_mode=False,
                ...     current_endpoint="127.0.0.1:7000")

        """
        from paddle.distributed.distribute_lookup_table import (
            find_distributed_lookup_table,
        )
        from paddle.distributed.transpiler.details import (
            VarsDistributed,
            find_op_by_output_arg,
        )

        err_msg = """

API is deprecated since 2.0.0 Please use FleetAPI instead.
WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler

        """
        print(err_msg, file=sys.stderr)

        if program is None:
            program = default_main_program()
        if startup_program is None:
            startup_program = default_startup_program()
        self.origin_program = program
        self.startup_program = startup_program
        self.origin_startup_program = self.startup_program.clone()

        if self.config.mode == "nccl2":
            assert isinstance(trainers, str)
            self.origin_program._trainers_endpoints = trainers.split(",")
            self.origin_program._nccl_comm_num = self.config.nccl_comm_num
            self.origin_program._use_hierarchical_allreduce = (
                self.config.use_hierarchical_allreduce
            )
            # check use_hierarchical_allreduce options
            if self.config.use_hierarchical_allreduce:
                trainers_num = len(self.origin_program._trainers_endpoints)
                # selected automatically
                if self.config.hierarchical_allreduce_inter_nranks <= 1:
                    self.config.hierarchical_allreduce_inter_nranks = (
                        core.get_cuda_device_count()
                    )

                assert (
                    trainers_num
                    > self.config.hierarchical_allreduce_inter_nranks
                ), (
                    f"trainers_num:{trainers_num} < hierarchical_allreduce_inter_nranks:{self.config.hierarchical_allreduce_inter_nranks}"
                )

                assert (
                    trainers_num
                    % self.config.hierarchical_allreduce_inter_nranks
                    == 0
                ), (
                    f"trainers_num:{trainers_num} mod hierarchical_allreduce_inter_nranks:{self.config.hierarchical_allreduce_inter_nranks} != 0"
                )

                self.origin_program._hierarchical_allreduce_inter_nranks = int(
                    self.config.hierarchical_allreduce_inter_nranks
                )

            self._transpile_nccl2(
                trainer_id,
                trainers,
                current_endpoint,
                startup_program=startup_program,
                wait_port=self.config.wait_port,
            )
            return

        if self.config.mode == "collective":
            self._transpile_collective(
                collective_mode=self.config.collective_mode,
                trainer_id=trainer_id,
                trainers=trainers,
                current_endpoint=current_endpoint,
                startup_program=startup_program,
                main_program=program,
                wait_port=self.config.wait_port,
            )
            return

        self.trainer_num = trainers
        self.sync_mode = sync_mode
        self.trainer_id = trainer_id
        pserver_endpoints = pservers.split(",")
        self.pserver_endpoints = pserver_endpoints
        self.vars_overview = VarsDistributed()
        self.optimize_ops, self.params_grads = self._get_optimize_pass()

        ps_dispatcher = self.config.split_method(self.pserver_endpoints)
        self.table_name = find_distributed_lookup_table(self.origin_program)
        self.has_distributed_lookup_table = self.table_name is not None
        self.param_name_to_grad_name = {}
        self.grad_name_to_param_name = {}
        for param_var, grad_var in self.params_grads:
            self.param_name_to_grad_name[param_var.name] = grad_var.name
            self.grad_name_to_param_name[grad_var.name] = param_var.name

        # get all sparse update ops
        self.sparse_update_ops = self._get_all_remote_sparse_update_op(
            self.origin_program
        )
        # use_sparse_update_param_name -> split_height_section
        self.sparse_param_to_height_sections = {}
        self.need_delete_optimize_vars = []

        # add distributed attrs to program
        self.origin_program._is_distributed = True
        self.origin_program._endpoints = self.pserver_endpoints
        self.origin_program._ps_endpoint = current_endpoint
        self.origin_program._is_chief = self.trainer_id == 0
        self.origin_program._distributed_lookup_table = (
            self.table_name if self.table_name else None
        )

        # split and create vars, then put split vars in dicts for later use.
        # step 1: split and create vars, then put split vars in dicts for later use.
        self._init_splited_vars()

        # step 2: insert send op to send gradient vars to parameter servers
        ps_dispatcher.reset()
        send_vars = []

        # in general cases, the number of pservers is times of 2, and this
        # will lead to uneven distribution among weights and bias:
        #       fc_w@GRAD_trainer_0, fc_w@GRAD_trainer_1 --> pserver1
        #       fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2
        # shuffle the map will avoid the uneven distribution above
        grad_var_mapping_items = list(self.grad_var_mapping.items())

        if not self.config.slice_var_up:
            np.random.seed(self.origin_program.random_seed)
            np.random.shuffle(grad_var_mapping_items)

        self.grad_name_to_send_dummy_out = {}

        for grad_varname, splited_vars in grad_var_mapping_items:
            eplist = ps_dispatcher.dispatch(splited_vars)

            if not self.config.slice_var_up:
                assert len(splited_vars) == 1

            splited_grad_varname = grad_varname
            if len(splited_vars) == 1:
                splited_grad_varname = splited_vars[0].name
                index = find_op_by_output_arg(
                    program.global_block(), splited_grad_varname, reverse=True
                )

            elif len(splited_vars) > 1:
                orig_var = program.global_block().vars[splited_grad_varname]
                index = find_op_by_output_arg(
                    program.global_block(), splited_grad_varname, reverse=True
                )

                if not self.config.runtime_split_send_recv:
                    self._insert_split_op(
                        program, orig_var, index, splited_vars
                    )
                    index += 1
            else:
                AssertionError(
                    "Can not insert the send op by original variable name :",
                    splited_grad_varname,
                )

            if splited_vars[0].type == core.VarDesc.VarType.SELECTED_ROWS:
                sparse_param_name = self.grad_name_to_param_name[grad_varname]
                if self._is_input_of_remote_sparse_update_op(sparse_param_name):
                    self.sparse_param_to_height_sections[sparse_param_name] = [
                        splited_var.shape[0] for splited_var in splited_vars
                    ]

            dummy_output = program.global_block().create_var(
                name=framework.generate_control_dev_var_name()
            )
            self.grad_name_to_send_dummy_out[grad_varname] = dummy_output

            if self.config.runtime_split_send_recv:
                send_input_vars = [
                    program.global_block().vars[splited_grad_varname]
                ]
                sections = self._get_splited_var_sections(splited_vars)

                if self.config.completely_not_async and self.trainer_num > 1:
                    send_varnames = [
                        f"{var.name}.trainer_{self.trainer_id}"
                        for var in splited_vars
                    ]
                else:
                    send_varnames = [var.name for var in splited_vars]
            else:
                send_input_vars = splited_vars
                sections = []
                send_varnames = []

            # get send op_role_var, if not split, the grad should have .trainer suffix
            # if split, grad should be the original grad var name (split_by_ref and send
            # will be on the same place). ParallelExecutor
            # will use op_role_var to get expected device place to run this op.
            program.global_block()._insert_op(
                index=index + 1,
                type="send",
                inputs={"X": send_input_vars},
                outputs={"Out": dummy_output},
                attrs={
                    "epmap": eplist,
                    "sections": sections,
                    "send_varnames": send_varnames,
                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                    OP_ROLE_VAR_ATTR_NAME: [
                        self.grad_name_to_param_name[grad_varname],
                        splited_grad_varname,
                    ],
                },
            )
            for _, var in enumerate(splited_vars):
                send_vars.append(var)

        send_barrier_out = program.global_block().create_var(
            name=framework.generate_control_dev_var_name()
        )
        if self.has_distributed_lookup_table:
            self.grad_name_to_send_dummy_out[self.table_name] = (
                program.global_block().create_var(
                    name=framework.generate_control_dev_var_name()
                )
            )
        input_deps = list(self.grad_name_to_send_dummy_out.values())

        if not self.sync_mode:
            lr_ops = self._get_lr_ops()
            if len(lr_ops) > 0 and self.counter_var:
                decay_dummy_output = program.global_block().create_var(
                    name=framework.generate_control_dev_var_name()
                )
                if self.config.runtime_split_send_recv:
                    # async mode, using communicator to merge and send
                    send_varnames = [self.counter_var.name]
                else:
                    send_varnames = []
                sections = []
                program.global_block().append_op(
                    type="send",
                    inputs={"X": self.counter_var},
                    outputs={"Out": decay_dummy_output},
                    attrs={
                        "epmap": pserver_endpoints,
                        "sections": sections,
                        "send_varnames": send_varnames,
                        "merge_add": True,
                        "use_send_handler": False,
                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                        OP_ROLE_VAR_ATTR_NAME: [
                            self.counter_var.name,
                            self.counter_var.name,
                        ],
                    },
                )
                input_deps.append(decay_dummy_output)

        if self.sync_mode:
            fetch_barrier_input = []

            program.global_block().append_op(
                type="send_barrier",
                inputs={"X": list(input_deps)},
                outputs={"Out": send_barrier_out},
                attrs={
                    "endpoints": pserver_endpoints,
                    "trainer_id": self.trainer_id,
                    "half_async": False,
                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                },
            )

            fetch_barrier_input.append(send_barrier_out)
        else:
            if self.config.runtime_split_send_recv and self.config.half_async:
                program.global_block().append_op(
                    type="send_barrier",
                    inputs={"X": list(input_deps)},
                    outputs={"Out": send_barrier_out},
                    attrs={
                        "endpoints": pserver_endpoints,
                        "trainer_id": self.trainer_id,
                        "half_async": True,
                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                    },
                )

        # step 3: insert recv op to receive parameters from parameter server
        recv_vars = []
        for _, var in enumerate(send_vars):
            recv_vars.append(self.grad_param_mapping[var])
        ps_dispatcher.reset()
        eplist = ps_dispatcher.dispatch(recv_vars)

        for i, ep in enumerate(eplist):
            self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i])
            self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])

            distributed_var = self.vars_overview.get_distributed_var_by_slice(
                recv_vars[i].name
            )
            distributed_var.endpoint = ep

        need_sparse_update_params = {}

        # step4: Concat the parameters splits together after recv.
        all_recv_outputs = []
        for param_varname, splited_var in self.param_var_mapping.items():
            eps = []
            table_names = []
            for var in splited_var:
                index = [v.name for v in recv_vars].index(var.name)
                eps.append(eplist[index])
                table_names.append(var.name)
            if self.sync_mode:
                recv_dep_in = send_barrier_out
            else:
                # connect deps to send op in async mode
                recv_dep_in = self.grad_name_to_send_dummy_out[
                    self.param_name_to_grad_name[param_varname]
                ]

            # get recv op_role_var, if not split, the grad should have .trainer suffix
            # if split, grad should be the original grad var name. ParallelExecutor
            # will use op_role_var to get expected device place to run this op.
            orig_grad_name = self.param_name_to_grad_name[param_varname]
            recv_op_role_var_name = orig_grad_name
            splited_trainer_grad = self.grad_var_mapping[orig_grad_name]
            if len(splited_trainer_grad) == 1:
                recv_op_role_var_name = splited_trainer_grad[0].name

            if param_varname in self.sparse_param_to_height_sections:
                for table_name in table_names:
                    distributed_var = (
                        self.vars_overview.get_distributed_var_by_slice(
                            table_name
                        )
                    )
                    distributed_var.vtype = "RemotePrefetch"

                need_sparse_update_params[param_varname] = (eps, table_names)
            else:
                recv_varnames = []
                if self.config.runtime_split_send_recv:
                    orig_param = program.global_block().vars[param_varname]
                    recv_varnames = [var.name for var in splited_var]
                    splited_var = [orig_param]
                all_recv_outputs.extend(splited_var)

                program.global_block().append_op(
                    type="recv",
                    inputs={"X": [recv_dep_in]},
                    outputs={"Out": splited_var},
                    attrs={
                        "epmap": eps,
                        "recv_varnames": recv_varnames,
                        "trainer_id": self.trainer_id,
                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                        OP_ROLE_VAR_ATTR_NAME: [
                            param_varname,
                            recv_op_role_var_name,
                        ],
                    },
                )

        self._update_remote_sparse_update_op(program, need_sparse_update_params)

        if self.sync_mode:
            # form a WAW dependency
            program.global_block().append_op(
                type="fetch_barrier",
                inputs={"X": fetch_barrier_input},
                outputs={"Out": all_recv_outputs},
                attrs={
                    "endpoints": pserver_endpoints,
                    "trainer_id": self.trainer_id,
                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                },
            )

        for param_varname, splited_var in self.param_var_mapping.items():
            if len(splited_var) <= 1:
                continue
            orig_param = program.global_block().vars[param_varname]
            if param_varname not in self.sparse_param_to_height_sections:
                if not self.config.runtime_split_send_recv:
                    program.global_block().append_op(
                        type="concat",
                        inputs={"X": splited_var},
                        outputs={"Out": [orig_param]},
                        attrs={
                            "axis": 0,
                            RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE,
                        },
                    )

        self._get_trainer_startup_program(recv_vars=recv_vars, eplist=eplist)

        if self.has_distributed_lookup_table:
            self._replace_lookup_table_op_with_prefetch(
                program, pserver_endpoints
            )
            self._split_table_grad_and_add_send_vars(program, pserver_endpoints)

        self._get_distributed_optimizer_vars()
        self.origin_program._parameters_on_pservers = self.vars_overview

    def _get_sparse_table_names(self):
        sparse_update_op_types = ["lookup_table", "nce"]

        sparse_table_names = []
        for op in self.origin_program.global_block().ops:
            if (
                op.type in sparse_update_op_types
                and op.attr('is_sparse') is True
            ):
                sparse_table_names.append(op.input("W")[0])
            if op.type == "distributed_lookup_table":
                sparse_table_names.append(op.input("W")[0])

        if self.has_distributed_lookup_table:
            sparse_table_names.append(self.table_name)

        return list(set(sparse_table_names))

    def _fake_init_sparsetable(self, sparse_table_names):
        # delete table init op
        from paddle.distributed.transpiler.details import delete_ops

        for table_name in sparse_table_names:
            table_var = self.startup_program.global_block().vars[table_name]
            table_param_init_op = []
            for op in self.startup_program.global_block().ops:
                if table_name in op.output_arg_names:
                    table_param_init_op.append(op)
            init_op_num = len(table_param_init_op)
            if init_op_num != 1:
                raise ValueError(
                    "table init op num should be 1, now is " + str(init_op_num)
                )
            table_init_op = table_param_init_op[0]
            self.startup_program.global_block().append_op(
                type="fake_init",
                inputs={},
                outputs={"Out": table_var},
                attrs={"shape": table_init_op.attr('shape')},
            )
            delete_ops(self.startup_program.global_block(), table_param_init_op)

    def _delete_trainer_optimizer(self, is_startup):
        from paddle.distributed.transpiler.details import delete_ops

        optimize_vars = []
        optimize_op_role_vars = []
        optimize_need_delete_vars = []

        for op in self.optimize_ops:
            optimize_vars.extend(op.input_arg_names)
            optimize_op_role_vars.extend(op.attr("op_role_var"))

        optimize_vars = list(set(optimize_vars))
        optimize_op_role_vars = list(set(optimize_op_role_vars))

        for var in optimize_vars:
            if var not in optimize_op_role_vars:
                optimize_need_delete_vars.append(var)
        need_delete_optimize_vars = list(set(optimize_need_delete_vars))

        if is_startup:
            init_ops = []
            for var in need_delete_optimize_vars:
                param_init_op = []
                for op in self.startup_program.global_block().ops:
                    if var in op.output_arg_names:
                        param_init_op.append(op)
                init_ops.extend(param_init_op)
            delete_ops(self.startup_program.global_block(), init_ops)

            for var in need_delete_optimize_vars:
                if self.startup_program.global_block().has_var(var):
                    self.startup_program.global_block()._remove_var(var)
        else:
            delete_ops(self.origin_program.global_block(), self.optimize_ops)
            for var in need_delete_optimize_vars:
                if self.origin_program.global_block().has_var(var):
                    self.origin_program.global_block()._remove_var(var)

    def get_trainer_program(self, wait_port=True):
        """
        Get transpiled trainer side program. The program on trainer side compared with origin program
        has following difference:

            - Delete optimizer related op, because parameter updated on Pserver
            - After the op which computed gradient of each parameter, add ``Send_op`` and ``Recv_op``

        Args:
            wait_port(bool): Whether to wait for the parameter server to be ready before returning to program,
            default is True

        Returns:
            Program: trainer side program.

        Examples:
            .. code-block:: python

                >>> # doctest: +REQUIRES(env:DISTRIBUTED)
                >>> import paddle.distributed.transpiler as transpiler
                >>> # this is an example, find available endpoints in your case
                >>> pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                >>> trainer_id = 0
                >>> trainers = 4

                >>> t = transpiler.DistributeTranspiler()
                >>> t.transpile(trainer_id, trainers=trainers, pservers=pserver_endpoints)
                >>> trainer_program = t.get_trainer_program()

        """
        # remove optimize ops and add a send op to main_program
        # FIXME(typhoonzero): Also ops like clip_gradient, lrn_decay?
        from paddle.distributed.fleet.base.private_helper_function import (
            wait_server_ready,
        )
        from paddle.distributed.transpiler.details import delete_ops

        self._delete_trainer_optimizer(is_startup=True)
        sparse_table_names = self._get_sparse_table_names()
        self._fake_init_sparsetable(sparse_table_names)

        lr_ops = self._get_lr_ops()
        delete_ops(self.origin_program.global_block(), lr_ops)
        self._delete_trainer_optimizer(is_startup=False)

        self.origin_program.__str__()
        self.startup_program.__str__()

        if wait_port:
            wait_server_ready(self.pserver_endpoints)

        return self.origin_program

    def _get_trainer_startup_program(self, recv_vars, eplist):
        """
        Get transpiled trainer side startup program.

        Args:
            recv_vars (list): Variable list to recv for current trainer_id
            eplist (list): A list of strings indicating

        Returns:
            Program: trainer side startup program.
        """
        startup_program = self.startup_program

        # FIXME(gongwb): delete not need ops.
        # note that: some parameter is not trainable and those ops can't be deleted.
        sparse_table_names = self._get_sparse_table_names()

        # self._fake_init_sparsetable(sparse_table_names)
        # self._delete_trainer_optimizer(is_startup=True)

        for varname, splited_var in self.param_var_mapping.items():
            if varname in sparse_table_names:
                continue
            # Get the eplist of recv vars
            eps = []
            for var in splited_var:
                index = [v.name for v in recv_vars].index(var.name)
                eps.append(eplist[index])

            for var in splited_var:
                if startup_program.global_block().has_var(var.name):
                    continue

                startup_program.global_block().create_var(
                    name=var.name,
                    persistable=False,
                    type=var.type,
                    dtype=var.dtype,
                    shape=var.shape,
                    lod_level=var.lod_level,
                )

            op = startup_program.global_block().append_op(
                type="recv",
                inputs={"X": []},
                outputs={"Out": splited_var},
                attrs={
                    "epmap": eps,
                    "trainer_id": self.trainer_id,
                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                },
            )

        fetch_barrier_out = startup_program.global_block().create_var(
            name=framework.generate_control_dev_var_name()
        )
        startup_program.global_block().append_op(
            type="fetch_barrier",
            inputs={},
            outputs={"Out": fetch_barrier_out},
            attrs={
                "endpoints": self.pserver_endpoints,
                "trainer_id": self.trainer_id,
                RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
            },
        )

        for varname, splited_var in self.param_var_mapping.items():
            if varname in sparse_table_names:
                continue
            # add concat ops to merge split parameters received from parameter servers.
            if len(splited_var) <= 1:
                continue
            # NOTE: if enable memory optimization, origin vars maybe removed.
            if varname in startup_program.global_block().vars:
                orig_param = startup_program.global_block().vars[varname]
            else:
                origin_param_var = self.origin_program.global_block().vars[
                    varname
                ]
                orig_param = startup_program.global_block().create_var(
                    name=varname,
                    persistable=origin_param_var.persistable,
                    type=origin_param_var.type,
                    dtype=origin_param_var.dtype,
                    shape=origin_param_var.shape,
                )
            startup_program.global_block().append_op(
                type="concat",
                inputs={"X": splited_var},
                outputs={"Out": [orig_param]},
                attrs={"axis": 0},
            )

        return startup_program

    def get_pserver_program(self, endpoint):
        """
        Get parameter server side program.The program on pserver side compared with origin program
        has following difference:

            - Only the following op is included: optimize-related op and communication-related op
            - NO.0 block only has variable definitions and ``listen_and_serv_op``
            - Every variable which need to be updated has a unique block

        Args:
            endpoint (str): current parameter server endpoint.

        Returns:
            Program: the program for current parameter server to run.

        Examples:
            .. code-block:: python

                >>> # doctest: +REQUIRES(env:DISTRIBUTED)
                >>> import paddle.distributed.transpiler as transpiler
                >>> # this is an example, find available endpoints in your case
                >>> pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                >>> current_endpoint = "192.168.0.1:6174"
                >>> trainer_id = 0
                >>> trainers = 4

                >>> t = transpiler.DistributeTranspiler()
                >>> t.transpile(
                ...     trainer_id, pservers=pserver_endpoints, trainers=trainers)

                >>> pserver_program = t.get_pserver_program(current_endpoint)

        """
        # TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers.
        # NOTE: assume blocks of the same variable is not distributed
        # on the same pserver, only change param/grad varnames for
        # trainers to fetch.
        sys.stderr.write(
            "get_pserver_program() is deprecated, call get_pserver_programs() to get pserver main and startup in a single call.\n"
        )
        # step1
        pserver_program = Program()
        pserver_program.random_seed = self.origin_program.random_seed
        pserver_program._copy_dist_param_info_from(self.origin_program)

        # step2: Create vars to receive vars at parameter servers.
        recv_inputs = []
        for v in self.param_grad_ep_mapping[endpoint]["params"]:
            self._clone_var(pserver_program.global_block(), v)
        for v in self.param_grad_ep_mapping[endpoint]["grads"]:
            # create vars for each trainer in global scope, so
            # we don't need to create them when grad arrives.
            # change client side var name to origin name by
            # removing ".trainer_%d" suffix
            suff_idx = v.name.find(".trainer_")
            if suff_idx >= 0:
                orig_var_name = v.name[:suff_idx]
            else:
                orig_var_name = v.name
            # NOTE: single_trainer_var must be created for multi-trainer
            # case to merge grads from multiple trainers
            single_trainer_var = pserver_program.global_block().create_var(
                name=orig_var_name,
                persistable=True,
                type=v.type,
                dtype=v.dtype,
                shape=v.shape,
            )
            if (
                self.sync_mode
                or self.config.completely_not_async
                and self.trainer_num > 1
            ):
                for trainer_id in range(self.trainer_num):
                    var = pserver_program.global_block().create_var(
                        name=f"{orig_var_name}.trainer_{trainer_id}",
                        persistable=False,
                        type=v.type,
                        dtype=v.dtype,
                        shape=v.shape,
                    )
                    recv_inputs.append(var)
            else:
                recv_inputs.append(single_trainer_var)

        # step 3
        # Create a union-find data structure from optimize ops,
        # If two ops are connected, we could add these two ops
        # into one set.
        ufind = self._create_ufind(self.optimize_ops)
        # step 3.2
        # Iterate through the ops and append optimize op which
        # located on current pserver
        opt_op_on_pserver = []
        for _, op in enumerate(self.optimize_ops):
            if self._is_optimizer_op(op) and self._is_opt_op_on_pserver(
                endpoint, op
            ):
                opt_op_on_pserver.append(op)
        # step 3.3
        # prepare if dc asgd is enabled
        if self.config.enable_dc_asgd is True:
            assert self.sync_mode is False
            self.param_bak_list = []
            # add param_bak for each trainer
            for p in self.param_grad_ep_mapping[endpoint]["params"]:
                # each parameter should have w_bak for each trainer id
                for i in range(self.trainer_num):
                    param_bak_name = f"{p.name}.trainer_{i}_bak"
                    tmpvar = pserver_program.global_block().create_var(
                        # NOTE: this var name format is used in `request_get_handler`
                        name=param_bak_name,
                        type=p.type,
                        shape=p.shape,
                        dtype=p.dtype,
                    )
                    self.param_bak_list.append((p, tmpvar))

        # step 3.4
        # Iterate through the ops, and if an op and the optimize ops
        # which located on current pserver are in one set, then
        # append it into the sub program.

        global_ops = []

        # sparse grad name to param name
        sparse_grad_to_param = []

        def __append_optimize_op__(
            op, block, grad_to_block_id, merged_var, lr_ops
        ):
            if self._is_optimizer_op(op):
                self._append_pserver_ops(
                    block,
                    op,
                    endpoint,
                    grad_to_block_id,
                    self.origin_program,
                    merged_var,
                    sparse_grad_to_param,
                )
            elif op not in lr_ops:
                self._append_pserver_non_opt_ops(block, op)

        def __clone_lr_op_sub_block__(op, program, lr_block):
            if not op.has_attr('sub_block'):
                return

            origin_block_desc = op.attr('sub_block')
            origin_block = self.origin_program.block(origin_block_desc.id)
            assert isinstance(origin_block, Block)
            # we put the new sub block to new block to follow the block
            # hierarchy of the original blocks
            new_sub_block = program._create_block(lr_block.idx)

            # clone vars
            for var in origin_block.vars:
                new_sub_block._clone_variable(var)

            # clone ops
            for origin_op in origin_block.ops:
                cloned_op = self._clone_lr_op(program, new_sub_block, origin_op)
                # clone sub_block of op
                __clone_lr_op_sub_block__(cloned_op, program, new_sub_block)

            # reset the block of op
            op._set_attr('sub_block', new_sub_block)

        # append lr decay ops to the child block if exists
        lr_ops = self._get_lr_ops()
        # record optimize blocks and we can run them on pserver parallel
        optimize_blocks = []

        lr_decay_block_id = -1
        if len(lr_ops) > 0:
            lr_decay_block = pserver_program._create_block(
                pserver_program.num_blocks - 1
            )
            optimize_blocks.append(lr_decay_block)
            for _, op in enumerate(lr_ops):
                cloned_op = self._append_pserver_non_opt_ops(lr_decay_block, op)
                # append sub blocks to pserver_program in lr_decay_op
                __clone_lr_op_sub_block__(
                    cloned_op, pserver_program, lr_decay_block
                )
            lr_decay_block_id = lr_decay_block.idx

        # append op to the current block
        grad_to_block_id = []
        pre_block_idx = pserver_program.num_blocks - 1
        for idx, opt_op in enumerate(opt_op_on_pserver):
            per_opt_block = pserver_program._create_block(pre_block_idx)
            optimize_blocks.append(per_opt_block)
            optimize_target_param_name = opt_op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
            # append grad merging ops before clip and weight decay
            # e.g. merge grad -> L2Decay op -> clip op -> optimize
            merged_var = None
            for _, op in enumerate(self.optimize_ops):
                # find the origin grad var before clipping/L2Decay,
                # merged_var should be the input var name of L2Decay
                grad_varname_for_block = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
                if (
                    op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
                    == optimize_target_param_name
                ):
                    merged_var = self._append_pserver_grad_merge_ops(
                        per_opt_block,
                        grad_varname_for_block,
                        endpoint,
                        grad_to_block_id,
                        self.origin_program,
                    )
                    if merged_var:
                        break  # append optimize op once then append other ops.
            if merged_var:
                for _, op in enumerate(self.optimize_ops):
                    # optimizer is connected to itself
                    if (
                        op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
                        == optimize_target_param_name
                        and op not in global_ops
                    ):
                        log(
                            "append opt op: ",
                            op.type,
                            op.input_arg_names,
                            merged_var,
                        )
                        __append_optimize_op__(
                            op,
                            per_opt_block,
                            grad_to_block_id,
                            merged_var,
                            lr_ops,
                        )

        # dedup grad to ids list
        grad_to_block_id = list(set(grad_to_block_id))
        # append global ops
        if global_ops:
            opt_state_block = pserver_program._create_block(
                pserver_program.num_blocks - 1
            )
            optimize_blocks.append(opt_state_block)
            for glb_op in global_ops:
                __append_optimize_op__(
                    glb_op, opt_state_block, grad_to_block_id, None, lr_ops
                )

        # process distributed lookup_table
        prefetch_var_name_to_block_id = []
        if self.has_distributed_lookup_table:
            pserver_index = self.pserver_endpoints.index(endpoint)
            table_opt_block = self._create_table_optimize_block(
                pserver_index, pserver_program, pre_block_idx, grad_to_block_id
            )
            optimize_blocks.append(table_opt_block)
            lookup_table_var_name_to_block_id = self._create_prefetch_block(
                pserver_index, pserver_program, table_opt_block
            )
            checkpoint_block_id = self._create_checkpoint_save_block(
                pserver_program, table_opt_block.idx
            )

            pserver_program._distributed_lookup_table = self.table_name
            prefetch_var_name_to_block_id.extend(
                lookup_table_var_name_to_block_id
            )

        if len(optimize_blocks) == 0:
            logging.warning(
                "pserver [" + str(endpoint) + "] has no optimize block!!"
            )
            pre_block_idx = pserver_program.num_blocks - 1
            empty_block = pserver_program._create_block(pre_block_idx)
            optimize_blocks.append(empty_block)

        # In some case, some parameter server will have no parameter to optimize
        # So we give an empty optimize block to parameter server.
        attrs = {
            "optimize_blocks": optimize_blocks,
            "endpoint": endpoint,
            "pserver_id": self.pserver_endpoints.index(endpoint),
            "Fanin": self.trainer_num,
            "distributed_mode": self.distributed_mode,
            "grad_to_block_id": grad_to_block_id,
            "sparse_grad_to_param": sparse_grad_to_param,
            "lr_decay_block_id": lr_decay_block_id,
            "rpc_get_thread_num": self.server_config._rpc_get_thread_num,
            "rpc_send_thread_num": self.server_config._rpc_send_thread_num,
            "rpc_prefetch_thread_num": self.server_config._rpc_prefetch_thread_num,
        }

        if self.has_distributed_lookup_table:
            attrs['checkpint_block_id'] = checkpoint_block_id
        if self.config.enable_dc_asgd:
            attrs['dc_asgd'] = True

        if len(prefetch_var_name_to_block_id) > 0:
            attrs['prefetch_var_name_to_block_id'] = (
                prefetch_var_name_to_block_id
            )

        # step5 append the listen_and_serv op
        pserver_program.global_block().append_op(
            type="listen_and_serv",
            inputs={'X': recv_inputs},
            outputs={},
            attrs=attrs,
        )

        pserver_program._sync_with_cpp()
        # save pserver program to generate pserver side startup relatively.
        self.pserver_program = pserver_program
        return pserver_program

    def get_pserver_programs(self, endpoint):
        """
        Get pserver side main program and startup program for distributed training.
        The ``main_program`` returned by this function is consistent with the
        return value of the function ``get_pserver_program`` .

        Args:
            endpoint (str): current pserver endpoint.

        Returns:
            tuple: (main_program, startup_program), of type "Program"

        Examples:
            .. code-block:: python

                >>> # doctest: +REQUIRES(env:DISTRIBUTED)
                >>> import paddle.distributed.transpiler as transpiler
                >>> # this is an example, find available endpoints in your case
                >>> pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                >>> current_endpoint = "192.168.0.1:6174"
                >>> trainer_id = 0
                >>> trainers = 4

                >>> t = transpiler.DistributeTranspiler()
                >>> t.transpile(
                ...     trainer_id, pservers=pserver_endpoints, trainers=trainers)
                >>> pserver_program, pserver_startup_program = t.get_pserver_programs(current_endpoint)

        """
        pserver_prog = self.get_pserver_program(endpoint)
        pserver_startup = self.get_startup_program(
            endpoint, pserver_program=pserver_prog
        )
        return pserver_prog, pserver_startup

    def get_startup_program(
        self, endpoint, pserver_program=None, startup_program=None
    ):
        """
        **Deprecated**

        Get startup program for current parameter server.
        Modify operator input variables if there are variables that
        were split to several blocks.

        Args:
            endpoint (str): current pserver endpoint.
            pserver_program (Program): deprecated, call get_pserver_program first.
            startup_program (Program): deprecated, should pass startup_program
                when initializing

        Returns:
            Program: parameter server side startup program.

        Examples:
            .. code-block:: python

                >>> # doctest: +REQUIRES(env:DISTRIBUTED)
                >>> pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                >>> trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
                >>> current_endpoint = "192.168.0.1:6174"
                >>> trainer_id = 0
                >>> trainers = 4

                >>> t = paddle.distributed.transpiler.DistributeTranspiler()
                >>> t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
                >>> pserver_program = t.get_pserver_program(current_endpoint)
                >>> pserver_startup_program = t.get_startup_program(current_endpoint,
                ...                                                 pserver_program)

        """
        s_prog = Program()
        orig_s_prog = self.startup_program
        s_prog.random_seed = orig_s_prog.random_seed
        params = self.param_grad_ep_mapping[endpoint]["params"]

        def _get_splited_name_and_shape(varname):
            for idx, splited_param in enumerate(params):
                pname = splited_param.name
                if same_or_split_var(pname, varname) and varname != pname:
                    return pname, splited_param.shape
            return "", []

        # 1. create vars in pserver program to startup program
        pserver_vars = pserver_program.global_block().vars
        created_var_map = collections.OrderedDict()
        for _, var in pserver_vars.items():
            tmpvar = s_prog.global_block()._clone_variable(var)
            created_var_map[var.name] = tmpvar

        # 2. rename op outputs
        for op in orig_s_prog.global_block().ops:
            new_outputs = collections.OrderedDict()
            # do not append startup op if var is not on this pserver
            op_on_pserver = False
            # TODO(gongwb): remove this line.
            if op.type not in ["recv", "fetch_barrier", "concat"]:
                for key in op.output_names:
                    newname, _ = _get_splited_name_and_shape(op.output(key)[0])
                    if newname:
                        op_on_pserver = True
                        new_outputs[key] = created_var_map[newname]
                    elif op.output(key)[0] in pserver_vars:
                        op_on_pserver = True
                        new_outputs[key] = pserver_vars[op.output(key)[0]]

            if op_on_pserver:
                # most startup program ops have no inputs
                new_inputs = self._get_input_map_from_op(pserver_vars, op)

                if op.type in [
                    "gaussian_random",
                    "fill_constant",
                    "uniform_random",
                    "truncated_gaussian_random",
                ]:
                    op._set_attr("shape", list(new_outputs["Out"].shape))
                s_prog.global_block().append_op(
                    type=op.type,
                    inputs=new_inputs,
                    outputs=new_outputs,
                    attrs=op.all_attrs(),
                )
        if self.config.enable_dc_asgd:
            for p, p_bak in self.param_bak_list:
                startup_param_var = s_prog.global_block().vars[p.name]
                startup_tmpvar = s_prog.global_block().vars[p_bak.name]
                # copy init random value to param_bak
                s_prog.global_block().append_op(
                    type="assign",
                    inputs={"X": startup_param_var},
                    outputs={"Out": startup_tmpvar},
                )

        return s_prog

    # ====================== private transpiler functions =====================
    def _get_slice_var_info(self, slice_var):
        block_suffix = "block"
        block_idx = 0
        offset = 0
        is_slice = False

        orig_var_name, block_name, _ = self._get_varname_parts(slice_var.name)

        if not block_name:
            return is_slice, block_idx, offset

        block_idx = int(block_name.split(block_suffix)[1])
        skip_dim0 = 0
        slice_vars = self.param_var_mapping[orig_var_name]

        orig_dim1_flatten = 1

        if len(slice_vars[0].shape) >= 2:
            orig_dim1_flatten = reduce(
                lambda x, y: x * y, slice_vars[0].shape[1:]
            )

        for slice_var in slice_vars[:block_idx]:
            skip_dim0 += slice_var.shape[0]

        offset = skip_dim0 * orig_dim1_flatten
        is_slice = True
        return is_slice, block_idx, offset

    def _get_distributed_optimizer_vars(self):
        def _get_distributed_optimizer_var(endpoint):
            from paddle.distributed.transpiler.details import VarStruct

            opt_op_on_pserver = []
            for _, op in enumerate(self.optimize_ops):
                if self._is_optimizer_op(op) and self._is_opt_op_on_pserver(
                    endpoint, op
                ):
                    opt_op_on_pserver.append(op)

            for opt_op in opt_op_on_pserver:
                dist_var = None
                for key in opt_op.input_names:
                    if key == "Param":
                        param_name = opt_op.input(key)[0]
                        dist_var = self.vars_overview.get_distributed_var_by_origin_and_ep(
                            param_name, endpoint
                        )
                        break
                for key in opt_op.input_names:
                    if key in [
                        "Param",
                        "Grad",
                        "LearningRate",
                        "Beta1Tensor",
                        "Beta2Tensor",
                    ]:
                        continue
                    origin_var = self.origin_program.global_block().vars[
                        opt_op.input(key)[0]
                    ]
                    # update accumulator variable shape
                    new_shape = self._get_optimizer_input_shape(
                        opt_op.type, key, origin_var.shape, dist_var.slice.shape
                    )

                    if new_shape == dist_var.slice.shape:
                        splited_var = VarStruct(
                            name=origin_var.name,
                            shape=new_shape,
                            dtype=origin_var.dtype,
                            type=origin_var.type,
                            lod_level=origin_var.lod_level,
                            persistable=origin_var.persistable,
                        )

                        self.vars_overview.add_distributed_var(
                            origin_var=origin_var,
                            slice_var=splited_var,
                            is_slice=dist_var.is_slice,
                            block_id=dist_var.block_id,
                            offset=dist_var.offset,
                            vtype="Optimizer",
                            endpoint=endpoint,
                        )
                    else:
                        self.vars_overview.add_distributed_var(
                            origin_var=origin_var,
                            slice_var=origin_var,
                            is_slice=False,
                            block_id=0,
                            offset=0,
                            vtype="Optimizer",
                            endpoint=endpoint,
                        )

        for ep in self.pserver_endpoints:
            _get_distributed_optimizer_var(ep)

    def _update_dist_lookup_table_vars(
        self, param_list, grad_list, params_grads
    ):
        # TODO(wuyi): put find a way to put dist lookup table stuff all together.
        # update self.table_param_grad and self.trainer_side_table_grad_list
        program = self.origin_program
        if self.has_distributed_lookup_table:
            param_list = [
                param for param in param_list if param.name != self.table_name
            ]
            grad_list = [
                grad
                for grad in grad_list
                if grad.name != grad_var_name(self.table_name)
            ]
            self.table_param_grad = next(
                param_grad
                for param_grad in params_grads
                if param_grad[0].name == self.table_name
            )
            table_grad_var = self.table_param_grad[1]
            if self.sync_mode:
                self.trainer_side_table_grad_list = [
                    program.global_block().create_var(
                        name=f"{table_grad_var.name}.trainer_{self.trainer_id}.pserver_{index}",
                        type=table_grad_var.type,
                        shape=table_grad_var.shape,
                        dtype=table_grad_var.dtype,
                    )
                    for index in range(len(self.pserver_endpoints))
                ]
            else:
                self.trainer_side_table_grad_list = [
                    program.global_block().create_var(
                        name=f"{table_grad_var.name}.pserver_{index}",
                        type=table_grad_var.type,
                        shape=table_grad_var.shape,
                        dtype=table_grad_var.dtype,
                    )
                    for index in range(len(self.pserver_endpoints))
                ]
        return param_list, grad_list

    def _init_splited_vars(self):
        # update these mappings for further transpile:
        # 1. param_var_mapping: param var name -> [split params vars]
        # 2. grad_var_mapping: grad var name -> [split grads vars]
        # 3. grad_param_mapping: grad.blockx -> param.blockx
        # 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}

        param_list = []
        grad_list = []
        param_grad_set = set()
        for p, g in self.params_grads:
            # skip parameter marked not trainable
            if type(p) == Parameter and p.trainable is False:
                continue
            if p.name not in param_grad_set:
                param_list.append(p)
                param_grad_set.add(p.name)
            if g.name not in param_grad_set:
                grad_list.append(g)
                param_grad_set.add(g.name)

        param_list, grad_list = self._update_dist_lookup_table_vars(
            param_list, grad_list, self.params_grads
        )

        if self.config.slice_var_up:
            # when we slice var up into blocks, we will slice the var according to
            # pserver services' count. A pserver may have two or more listening ports.
            grad_blocks = slice_variable(
                grad_list,
                len(self.pserver_endpoints),
                self.config.min_block_size,
            )
            param_blocks = slice_variable(
                param_list,
                len(self.pserver_endpoints),
                self.config.min_block_size,
            )
        else:
            # when we do NOT slice var up into blocks, we will always slice params
            # grads into one block.
            grad_blocks = slice_variable(
                grad_list, 1, self.config.min_block_size
            )
            param_blocks = slice_variable(
                param_list, 1, self.config.min_block_size
            )
        assert len(grad_blocks) == len(param_blocks)

        # origin_param_name -> [splited_param_vars]
        self.param_var_mapping = self._create_vars_from_blocklist(
            self.origin_program, param_blocks
        )

        for orig_name, splited_vars in self.param_var_mapping.items():
            orig_var = self.origin_program.global_block().var(orig_name)

            for splited_var in splited_vars:
                is_slice, block_id, offset = self._get_slice_var_info(
                    splited_var
                )

                self.vars_overview.add_distributed_var(
                    origin_var=orig_var,
                    slice_var=splited_var,
                    block_id=block_id,
                    offset=offset,
                    is_slice=is_slice,
                    vtype="Param",
                )

        # origin_grad_name -> [splited_grad_vars]
        self.grad_var_mapping = self._create_vars_from_blocklist(
            self.origin_program,
            grad_blocks,
            add_trainer_suffix=self.trainer_num > 1,
        )
        # dict(grad_splited_var -> param_splited_var)
        self.grad_param_mapping = collections.OrderedDict()
        for g, p in zip(grad_blocks, param_blocks):
            g_name, g_bid, _ = g.split(":")
            p_name, p_bid, _ = p.split(":")
            self.grad_param_mapping[
                self.grad_var_mapping[g_name][int(g_bid)]
            ] = self.param_var_mapping[p_name][int(p_bid)]

        # create mapping of endpoint -> split var to create pserver side program
        self.param_grad_ep_mapping = collections.OrderedDict()
        [
            self.param_grad_ep_mapping.update({ep: {"params": [], "grads": []}})
            for ep in self.pserver_endpoints
        ]

    # transpiler function for dis lookup_table
    def _replace_lookup_table_op_with_prefetch(
        self, program, pserver_endpoints
    ):
        from paddle.distributed.transpiler.details import delete_ops

        # 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op
        self.all_in_ids_vars = []
        self.all_prefetch_input_vars = []
        self.all_prefetch_output_vars = []
        self.all_out_emb_vars = []
        lookup_table_op_index = -1

        continue_search_lookup_table_op = True
        while continue_search_lookup_table_op:
            continue_search_lookup_table_op = False
            all_ops = program.global_block().ops
            for op in all_ops:
                if (
                    op.type == LOOKUP_TABLE_TYPE
                    and self.table_name == op.input("W")[0]
                ):
                    if not op.attr('is_distributed'):
                        raise RuntimeError(
                            "lookup_table_op that lookup an distributed embedding table"
                            "should set is_distributed to true"
                        )
                    continue_search_lookup_table_op = True

                    lookup_table_op_index = (
                        lookup_table_op_index
                        if lookup_table_op_index != -1
                        else list(all_ops).index(op)
                    )
                    ids_name = op.input("Ids")
                    out_name = op.output("Out")

                    ids_var = program.global_block().vars[ids_name[0]]
                    self.all_in_ids_vars.append(ids_var)

                    out_var = program.global_block().vars[out_name[0]]
                    self.all_out_emb_vars.append(out_var)

                    # delete lookup_table_op
                    delete_ops(program.global_block(), [op])
                    # break for loop
                    break

        for index in range(len(self.pserver_endpoints)):
            in_var = program.global_block().create_var(
                name=str("prefetch_compress_in_tmp_" + str(index)),
                type=self.all_in_ids_vars[0].type,
                shape=self.all_in_ids_vars[0].shape,
                dtype=self.all_in_ids_vars[0].dtype,
            )
            self.all_prefetch_input_vars.append(in_var)

            out_var = program.global_block().create_var(
                name=str("prefetch_compress_out_tmp_" + str(index)),
                type=self.all_out_emb_vars[0].type,
                shape=self.all_out_emb_vars[0].shape,
                dtype=self.all_out_emb_vars[0].dtype,
            )
            self.all_prefetch_output_vars.append(out_var)

        # insert split_ids_op
        program.global_block()._insert_op(
            index=lookup_table_op_index,
            type="split_ids",
            inputs={'Ids': self.all_in_ids_vars},
            outputs={"Out": self.all_prefetch_input_vars},
        )

        # insert prefetch_op
        program.global_block()._insert_op(
            index=lookup_table_op_index + 1,
            type="prefetch",
            inputs={'X': self.all_prefetch_input_vars},
            outputs={"Out": self.all_prefetch_output_vars},
            attrs={
                "epmap": pserver_endpoints,
                # FIXME(qiao) temporarily disable this config because prefetch
                # is not act as other rpc op, it's more like a forward op
                # RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
            },
        )

        # insert concat_op
        program.global_block()._insert_op(
            index=lookup_table_op_index + 2,
            type="merge_ids",
            inputs={
                'Ids': self.all_in_ids_vars,
                'Rows': self.all_prefetch_input_vars,
                'X': self.all_prefetch_output_vars,
            },
            outputs={"Out": self.all_out_emb_vars},
        )

    def _split_table_grad_and_add_send_vars(self, program, pserver_endpoints):
        # 2. add split_ids_op and send_op to send gradient to pservers

        # there should only be one table_name
        all_ops = program.global_block().ops
        table_grad_name = grad_var_name(self.table_name)
        for op in all_ops:
            if table_grad_name in op.output_arg_names:
                op_index = list(all_ops).index(op)
                # insert split_ids_op
                program.global_block()._insert_op(
                    index=op_index + 1,
                    type="split_ids",
                    inputs={
                        'Ids': [program.global_block().vars[table_grad_name]]
                    },
                    outputs={"Out": self.trainer_side_table_grad_list},
                    attrs={RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE},
                )
                program.global_block()._insert_op(
                    index=op_index + 2,
                    type="send",
                    inputs={'X': self.trainer_side_table_grad_list},
                    outputs={
                        'Out': (
                            [self.grad_name_to_send_dummy_out[self.table_name]]
                            if self.sync_mode
                            else []
                        )
                    },
                    attrs={
                        "epmap": pserver_endpoints,
                        "trainer_id": self.trainer_id,
                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
                        OP_ROLE_VAR_ATTR_NAME: [
                            self.grad_name_to_param_name[table_grad_name],
                            table_grad_name,
                        ],
                    },
                )
                break

    def _create_prefetch_block(
        self, pserver_index, pserver_program, optimize_block
    ):
        # STEP: create prefetch block
        table_var = pserver_program.global_block().vars[self.table_name]
        prefetch_var_name_to_block_id = []
        prefetch_block = pserver_program._create_block(optimize_block.idx)
        trainer_ids = self.all_prefetch_input_vars[pserver_index]
        pserver_ids = pserver_program.global_block().create_var(
            name=trainer_ids.name,
            type=trainer_ids.type,
            shape=trainer_ids.shape,
            dtype=trainer_ids.dtype,
        )
        trainer_out = self.all_prefetch_output_vars[pserver_index]
        pserver_out = pserver_program.global_block().create_var(
            name=trainer_out.name,
            type=trainer_out.type,
            shape=trainer_out.shape,
            dtype=trainer_out.dtype,
        )
        prefetch_block.append_op(
            type="lookup_sparse_table",
            inputs={'Ids': pserver_ids, "W": table_var},
            outputs={"Out": pserver_out},
            attrs={
                "is_sparse": True,  # has no effect on lookup_table op
                "is_distributed": True,
                "padding_idx": -1,
            },
        )
        prefetch_var_name_to_block_id.append(
            trainer_ids.name + ":" + str(prefetch_block.idx)
        )
        return prefetch_var_name_to_block_id

    def _create_table_optimize_block(
        self, pserver_index, pserver_program, pre_block_idx, grad_to_block_id
    ):
        # STEP: create table optimize block
        table_opt_block = pserver_program._create_block(pre_block_idx)
        # create table param and grad var in pserver program
        # create table optimize block in pserver program
        table_opt_op = next(
            op
            for op in self.optimize_ops
            if 'Param' in op.input_names
            and op.input("Param")[0] == self.table_name
        )

        origin_param_var = self.origin_program.global_block().vars[
            self.table_name
        ]

        zero_dim = int(
            math.ceil(
                origin_param_var.shape[0] / float(len(self.pserver_endpoints))
            )
        )
        table_shape = list(origin_param_var.shape)
        table_shape[0] = zero_dim

        param_var = pserver_program.global_block().create_var(
            name=origin_param_var.name,
            shape=table_shape,
            dtype=origin_param_var.dtype,
            type=core.VarDesc.VarType.SELECTED_ROWS,
            persistable=True,
        )

        # parameter must be selected rows
        param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
        grad_var = pserver_program.global_block()._clone_variable(
            self.origin_program.global_block().vars[
                grad_var_name(self.table_name)
            ]
        )

        lr_var = pserver_program.global_block()._clone_variable(
            self.origin_program.global_block().vars[
                table_opt_op.input("LearningRate")[0]
            ]
        )

        if self.sync_mode:
            # create grad vars in pserver program
            table_grad_var = self.table_param_grad[1]
            pserver_side_table_grad_list = [
                pserver_program.global_block().create_var(
                    name=f"{table_grad_var.name}.trainer_{index}.pserver_{pserver_index}",
                    type=table_grad_var.type,
                    shape=table_grad_var.shape,
                    dtype=table_grad_var.dtype,
                )
                for index in range(self.trainer_num)
            ]

            # append sum op for pserver_side_table_grad_list
            table_opt_block.append_op(
                type="sum",
                inputs={"X": pserver_side_table_grad_list},
                outputs={"Out": [grad_var]},
                attrs={},
            )
        else:
            # in async_mode, for table gradient, it also need to be split to each parameter server
            origin_grad_name = grad_var.name
            splited_grad_name = self.trainer_side_table_grad_list[
                pserver_index
            ].name
            if not splited_grad_name.startswith(origin_grad_name):
                raise ValueError(
                    "origin_grad_var: "
                    + splited_grad_name
                    + " grad_var:"
                    + grad_var.name
                )
            grad_var = pserver_program.global_block()._rename_var(
                origin_grad_name, splited_grad_name
            )

        inputs = {
            "Param": [param_var],
            "Grad": [grad_var],
            "LearningRate": [lr_var],
        }
        outputs = {"ParamOut": [param_var]}
        # only support sgd now
        logging.warning(
            "distribute lookup table only support sgd optimizer, change it's optimizer to sgd instead of "
            + table_opt_op.type
        )
        table_opt_block.append_op(type="sgd", inputs=inputs, outputs=outputs)

        # add table parameter gradient and it's block id to grad_to_block_id
        grad_to_block_id.append(grad_var.name + ":" + str(table_opt_block.idx))

        return table_opt_block

    def _create_checkpoint_save_block(self, pserver_program, pre_block_idx):
        """
        create a new block to handle save checkpoint.
        """

        pserver_program.global_block().create_var(
            name="kLookupTablePath",
            persistable=True,
            type=core.VarDesc.VarType.RAW,
        )

        checkpoint_save_block = pserver_program._create_block(pre_block_idx)
        # this 'file_path' do not be used in save lookup table variable
        checkpoint_save_block.append_op(
            type='save',
            inputs={'X': [self.table_name]},
            outputs={},
            attrs={'file_path': "none"},
        )

        return checkpoint_save_block.idx

    def _create_vars_from_blocklist(
        self, program, block_list, add_trainer_suffix=False
    ):
        """
        Create vars for each split.
        NOTE: only grads need to be named for different trainers, use
              add_trainer_suffix to rename the grad vars.
        Args:
            program (ProgramDesc): ProgramDesc which gradients belong.
            block_list (list[(varname, block_id, block_size)]): List of gradient blocks.
            add_trainer_suffix (Bool): Add trainer suffix to new variable's name if set True.
        Returns:
            var_mapping (collections.OrderedDict(varname->[new_varname_variable])):A dict mapping
                from original var name to each var split.
        """

        # varname->[(block_id, current_block_size)]
        block_map = collections.OrderedDict()

        var_mapping = collections.OrderedDict()
        for block_str in block_list:
            varname, offset, size = block_str.split(":")
            if varname not in block_map:
                block_map[varname] = []
            block_map[varname].append((int(offset), int(size)))

        for varname, split in block_map.items():
            orig_var = program.global_block().var(varname)
            if len(split) == 1:
                if self.sync_mode and add_trainer_suffix:
                    new_var_name = f"{orig_var.name}.trainer_{self.trainer_id}"
                    program.global_block()._rename_var(varname, new_var_name)
                    var_mapping[varname] = [
                        program.global_block().var(new_var_name)
                    ]
                else:
                    var_mapping[varname] = [
                        program.global_block().var(orig_var.name)
                    ]
                continue
            var_mapping[varname] = []
            orig_shape = orig_var.shape
            orig_dim1_flatten = 1
            if len(orig_shape) >= 2:
                orig_dim1_flatten = reduce(
                    lambda x, y: x * y, orig_shape[1:], 1
                )

            for i, block in enumerate(split):
                size = block[1]
                rows = size // orig_dim1_flatten
                splited_shape = [rows]
                if len(orig_shape) >= 2:
                    splited_shape.extend(orig_shape[1:])
                new_var_name = ""
                if self.sync_mode and add_trainer_suffix:
                    new_var_name = (
                        f"{varname}.block{i}.trainer_{self.trainer_id}"
                    )
                else:
                    new_var_name = f"{varname}.block{i}"
                var = program.global_block().create_var(
                    name=new_var_name,
                    persistable=False,
                    dtype=orig_var.dtype,
                    type=orig_var.type,
                    shape=splited_shape,
                )  # flatten split var
                var_mapping[varname].append(var)
            program.global_block()._sync_with_cpp()
        return var_mapping

    def _clone_var(self, block, var, persistable=True):
        return block.create_var(
            name=var.name,
            shape=var.shape,
            dtype=var.dtype,
            type=var.type,
            lod_level=var.lod_level,
            persistable=persistable,
        )

    @staticmethod
    def _get_splited_var_sections(splited_vars):
        height_sections = []
        for v in splited_vars:
            height_sections.append(v.shape[0])
        return height_sections

    def _insert_split_op(self, program, orig_var, index, splited_vars):
        height_sections = self._get_splited_var_sections(splited_vars)

        if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS:
            sparse_param_name = self.grad_name_to_param_name[orig_var.name]
            if self._is_input_of_remote_sparse_update_op(sparse_param_name):
                self.sparse_param_to_height_sections[sparse_param_name] = (
                    height_sections
                )
            program.global_block()._insert_op(
                index=index + 1,
                type="split_selected_rows",
                inputs={"X": orig_var},
                outputs={"Out": splited_vars},
                attrs={
                    "height_sections": height_sections,
                    RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE,
                },
            )
        elif orig_var.type == core.VarDesc.VarType.DENSE_TENSOR:
            program.global_block()._insert_op(
                index=index + 1,
                type="split_byref",
                inputs={"X": orig_var},
                outputs={"Out": splited_vars},
                attrs={
                    "sections": height_sections,
                    RPC_OP_ROLE_ATTR_NAME: DIST_OP_ROLE_ATTR_VALUE,
                },
            )
        else:
            AssertionError(
                "Variable type should be in set [DENSE_TENSOR, SELECTED_ROWS]"
            )

    def _get_optimizer_input_shape(
        self, op_type, varkey, orig_shape, param_shape
    ):
        """
        Returns the shape for optimizer inputs that need to be reshaped when
        Param and Grad is split to multiple servers.
        """
        # HACK(typhoonzero): Should use functions of corresponding optimizer in
        # optimizer.py to get the shape, do not  bind this in the transpiler.
        if op_type == "adam":
            if varkey in ["Moment1", "Moment2"]:
                return param_shape
        elif op_type == "adagrad":
            if varkey == "Moment":
                return param_shape
        elif op_type == "adamax":
            if varkey in ["Moment", "InfNorm"]:
                return param_shape
        elif op_type in ["momentum", "lars_momentum"]:
            if varkey == "Velocity":
                return param_shape
        elif op_type == "rmsprop":
            if varkey in ["Moment", "MeanSquare"]:
                return param_shape
        elif op_type == "decayed_adagrad":
            if varkey == "Moment":
                return param_shape
        elif op_type == "ftrl":
            if varkey in ["SquaredAccumulator", "LinearAccumulator"]:
                return param_shape
        elif op_type == "sgd":
            pass
        else:
            raise ValueError(
                f"Not supported optimizer for distributed training: {op_type}"
            )
        return orig_shape

    def _get_varname_parts(self, varname):
        # returns origin, blockid, trainerid
        orig_var_name = ""
        trainer_part = ""
        block_part = ""
        trainer_idx = varname.find(".trainer_")
        if trainer_idx >= 0:
            trainer_part = varname[trainer_idx + 1 :]
        else:
            trainer_idx = len(varname)
        block_index = varname.find(".block")
        if block_index >= 0:
            block_part = varname[block_index + 1 : trainer_idx]
        else:
            block_index = len(varname)
        orig_var_name = varname[0 : min(block_index, trainer_idx)]
        return orig_var_name, block_part, trainer_part

    def _orig_varname(self, varname):
        orig, _, _ = self._get_varname_parts(varname)
        return orig

    def _append_pserver_grad_merge_ops(
        self,
        optimize_block,
        grad_varname_for_block,
        endpoint,
        grad_to_block_id,
        origin_program,
    ):
        program = optimize_block.program
        pserver_block = program.global_block()
        grad_block = None
        for g in self.param_grad_ep_mapping[endpoint]["grads"]:
            if self._orig_varname(g.name) == self._orig_varname(
                grad_varname_for_block
            ):
                grad_block = g
                break
        if not grad_block:
            # do not append this op if current endpoint
            # is not dealing with this grad block
            return None
        orig_varname, block_name, trainer_name = self._get_varname_parts(
            grad_block.name
        )
        if block_name:
            merged_var_name = '.'.join([orig_varname, block_name])
        else:
            merged_var_name = orig_varname

        merged_var = pserver_block.vars[merged_var_name]
        grad_to_block_id.append(merged_var.name + ":" + str(optimize_block.idx))
        if (
            self.sync_mode
            or self.config.completely_not_async
            and self.trainer_num > 1
        ):
            vars2merge = []
            for i in range(self.trainer_num):
                per_trainer_name = f"{merged_var_name}.trainer_{i}"
                vars2merge.append(pserver_block.vars[per_trainer_name])
            optimize_block.append_op(
                type="sum",
                inputs={"X": vars2merge},
                outputs={"Out": merged_var},
                attrs={},
            )
            optimize_block.append_op(
                type="scale",
                inputs={"X": merged_var},
                outputs={"Out": merged_var},
                attrs={"scale": 1.0 / float(self.trainer_num)},
            )
        return merged_var

    def _append_dc_asgd_ops(self, block, param_var, grad_var):
        # NOTE: can not use grammar candy here, should put ops in specific block
        local_param_bak = block.create_var(
            name=f"{param_var.name}.local_bak",
            shape=param_var.shape,
            type=param_var.type,
            dtype=param_var.dtype,
            persistable=False,
        )
        # trainer_id_var is block local
        trainer_id_var = block.create_var(
            name="@TRAINER_ID@",
            type=core.VarDesc.VarType.DENSE_TENSOR,
            dtype=core.VarDesc.VarType.INT64,
            shape=[1],
            persistable=False,
        )

        # ref_inputs = [x[1] for x in self.param_bak_list]
        ref_inputs = []
        for p, p_bak in self.param_bak_list:
            if p.name == param_var.name:
                ref_inputs.append(p_bak)
        block.append_op(
            type="ref_by_trainer_id",
            inputs={"X": ref_inputs, "TrainerId": trainer_id_var},
            outputs={"Out": local_param_bak},
        )

        def __create_temp_var__():
            return block.create_var(
                name=unique_name.generate("tmp_dc_output"),
                shape=param_var.shape,
                type=param_var.type,
                dtype=param_var.dtype,
                persistable=False,
            )

        o1 = __create_temp_var__()
        block.append_op(
            type="elementwise_sub",
            inputs={"X": param_var, "Y": local_param_bak},
            outputs={"Out": o1},
        )
        o2 = __create_temp_var__()
        block.append_op(
            type="elementwise_mul",
            inputs={"X": o1, "Y": grad_var},
            outputs={"Out": o2},
        )
        o3 = __create_temp_var__()
        block.append_op(
            type="elementwise_mul",
            inputs={"X": o2, "Y": grad_var},
            outputs={"Out": o3},
        )
        # TODO(typhoonzero): append scale
        o4 = __create_temp_var__()
        block.append_op(
            type="elementwise_add",
            inputs={"X": grad_var, "Y": o3},
            outputs={"Out": o4},
        )
        return o4

    def _append_pserver_ops(
        self,
        optimize_block,
        opt_op,
        endpoint,
        grad_to_block_id,
        origin_program,
        merged_var,
        sparse_grad_to_param,
    ):
        program = optimize_block.program
        pserver_block = program.global_block()
        new_inputs = collections.OrderedDict()

        def _get_param_block(opt_op):
            # param is already created on global program
            param_block = None
            for p in self.param_grad_ep_mapping[endpoint]["params"]:
                if same_or_split_var(p.name, opt_op.input("Param")[0]):
                    param_block = p
                    break
            return param_block

        if self.config.enable_dc_asgd:
            param_var = _get_param_block(opt_op)
            dc = self._append_dc_asgd_ops(optimize_block, param_var, merged_var)

        for key in opt_op.input_names:
            if key == "Grad":
                if self.config.enable_dc_asgd:
                    new_inputs[key] = dc
                else:
                    # Note!! This is for l2decay on sparse gradient, because it will create a new tensor for
                    # decayed gradient but not inplace modify the origin one
                    origin_grad_name = opt_op.input(key)[0]
                    if (
                        core.kNewGradSuffix() in origin_grad_name
                        and pserver_block.has_var(origin_grad_name)
                    ):
                        new_grad = pserver_block.var(origin_grad_name)
                        new_inputs[key] = new_grad
                    else:
                        new_inputs[key] = merged_var
            elif key == "Param":
                param_block = _get_param_block(opt_op)
                if not param_block:
                    return
                tmpvar = pserver_block.create_var(
                    name=param_block.name,
                    persistable=True,
                    dtype=param_block.dtype,
                    shape=param_block.shape,
                )
                new_inputs[key] = tmpvar
            elif key == "LearningRate":
                # learning rate variable has already be created by non-optimize op,
                # don't create it once again.
                lr_varname = opt_op.input(key)[0]
                if lr_varname in pserver_block.vars:
                    new_inputs[key] = pserver_block.vars[opt_op.input(key)[0]]
                else:
                    origin_var = origin_program.global_block().vars[lr_varname]
                    tmpvar = pserver_block.create_var(
                        name=origin_var.name,
                        persistable=origin_var.persistable,
                        dtype=origin_var.dtype,
                        shape=origin_var.shape,
                    )
                    new_inputs[key] = tmpvar

        for key in opt_op.input_names:
            new_shape = None
            if key in [
                "Param",
                "Grad",
                "LearningRate",
                "Beta1Tensor",
                "Beta2Tensor",
            ]:
                continue
            var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
            param_var = new_inputs["Param"]
            # update accumulator variable shape
            new_shape = self._get_optimizer_input_shape(
                opt_op.type, key, var.shape, param_var.shape
            )
            tmpvar = pserver_block.create_var(
                name=var.name,
                persistable=var.persistable,
                dtype=var.dtype,
                shape=new_shape,
            )
            new_inputs[key] = tmpvar

        # change output's ParamOut variable
        outputs = self._get_output_map_from_op(
            self.origin_program.global_block().vars, opt_op
        )
        outputs["ParamOut"] = new_inputs["Param"]
        optimize_block.append_op(
            type=opt_op.type,
            inputs=new_inputs,
            outputs=outputs,
            attrs=opt_op.all_attrs(),
        )

        # record sparse grad to param name
        if new_inputs["Grad"].type == core.VarDesc.VarType.SELECTED_ROWS:
            sparse_grad_to_param.append(
                str(new_inputs["Grad"].name)
                + ":"
                + str(new_inputs["Param"].name)
            )

    def _get_pserver_grad_param_var(self, var, var_dict):
        """
        Return pserver side grad/param variable, return None
        if the variable is not grad/param, e.g.

            a@GRAD -> a@GRAD.block0
            a@GRAD -> a@GRAD (a is not split)
            fc_0.w_0 -> fc_0.w_0.block_0
            fc_0.w_0 -> fc_0.w_0 (weight is not split)
            _generated_var_123 -> None
        """
        grad_block = None
        for _, g in var_dict.items():
            if self._orig_varname(g.name) == self._orig_varname(var.name):
                # skip per trainer vars
                if g.name.find(".trainer_") == -1:
                    # only param or grads have split blocks
                    if (
                        self._orig_varname(g.name)
                        in self.grad_name_to_param_name
                        or self._orig_varname(g.name)
                        in self.param_name_to_grad_name
                    ):
                        grad_block = g
                        break
        return grad_block

    def _clone_lr_op(self, program, block, op):
        inputs = self._get_input_map_from_op(
            self.origin_program.global_block().vars, op
        )
        for key, varlist in inputs.items():
            if not isinstance(varlist, list):
                varlist = [varlist]
            for var in varlist:
                if var not in program.global_block().vars:
                    block._clone_variable(var)

        outputs = self._get_output_map_from_op(
            self.origin_program.global_block().vars, op
        )
        for key, varlist in outputs.items():
            if not isinstance(varlist, list):
                varlist = [varlist]
            for var in varlist:
                if var not in program.global_block().vars:
                    block._clone_variable(var)

        return block.append_op(
            type=op.type, inputs=inputs, outputs=outputs, attrs=op.all_attrs()
        )

    def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
        program = optimize_block.program
        # Append the ops for parameters that do not need to be optimized/updated
        inputs = self._get_input_map_from_op(
            self.origin_program.global_block().vars, opt_op
        )
        for key, varlist in inputs.items():
            if not isinstance(varlist, list):
                varlist = [varlist]
            for i in range(len(varlist)):
                var = varlist[i]
                # for ops like clipping and weight decay, get the split var (xxx.block0)
                # for inputs/outputs
                grad_block = self._get_pserver_grad_param_var(
                    var, program.global_block().vars
                )
                if grad_block:
                    varlist[i] = grad_block
                elif var.name not in program.global_block().vars:
                    tmpvar = program.global_block()._clone_variable(var)
                    varlist[i] = tmpvar
                else:
                    varlist[i] = program.global_block().vars[var.name]
            inputs[key] = varlist

        outputs = self._get_output_map_from_op(
            self.origin_program.global_block().vars, opt_op
        )
        for key, varlist in outputs.items():
            if not isinstance(varlist, list):
                varlist = [varlist]
            for i in range(len(varlist)):
                var = varlist[i]
                grad_block = self._get_pserver_grad_param_var(
                    var, program.global_block().vars
                )
                if grad_block:
                    varlist[i] = grad_block
                elif var.name not in program.global_block().vars:
                    tmpvar = program.global_block()._clone_variable(var)
                    varlist[i] = tmpvar
                else:
                    varlist[i] = program.global_block().vars[var.name]
            outputs[key] = varlist

        return optimize_block.append_op(
            type=opt_op.type,
            inputs=inputs,
            outputs=outputs,
            attrs=opt_op.all_attrs(),
        )

    def _is_op_connected(self, op1, op2):
        # If one op's input is another op's output or
        # one op's output is another op's input, we say
        # the two operator is connected.
        if set(op1.desc.output_arg_names()) & set(
            op2.desc.input_arg_names()
        ) or set(op1.desc.input_arg_names()) & set(op2.desc.output_arg_names()):
            return True
        return False

    def _create_ufind(self, optimize_ops):
        # Create a unit find data struct by optimize ops
        from paddle.distributed.transpiler.details import UnionFind

        ufind = UnionFind(optimize_ops)
        for i in range(len(optimize_ops)):
            for j in range(i, len(optimize_ops)):
                op1 = optimize_ops[i]
                op2 = optimize_ops[j]
                if self._is_op_connected(op1, op2):
                    ufind.union(op1, op2)
        return ufind

    def _is_optimizer_op(self, op):
        if "Param" in op.input_names and "LearningRate" in op.input_names:
            return True
        return False

    def _is_opt_op_on_pserver(self, endpoint, op):
        param_names = [
            p.name for p in self.param_grad_ep_mapping[endpoint]["params"]
        ]
        if op.input("Param")[0] in param_names:
            return True
        else:
            for n in param_names:
                param = op.input("Param")[0]
                if same_or_split_var(n, param) and n != param:
                    return True
            return False

    def _get_input_map_from_op(self, varmap, op):
        """Returns a dict from op input name to the vars in varmap."""
        iomap = collections.OrderedDict()
        for key in op.input_names:
            vars = []
            for varname in op.input(key):
                vars.append(varmap[varname])
            if len(vars) == 1:
                iomap[key] = vars[0]
            else:
                iomap[key] = vars
        return iomap

    def _get_output_map_from_op(self, varmap, op):
        """Returns a dict from op output name to the vars in varmap."""
        iomap = collections.OrderedDict()
        for key in op.output_names:
            vars = []
            for varname in op.output(key):
                vars.append(varmap[varname])
            if len(vars) == 1:
                iomap[key] = vars[0]
            else:
                iomap[key] = vars
        return iomap

    def _get_lr_ops(self):
        lr_ops = []
        block = self.origin_program.global_block()
        for index, op in enumerate(block.ops):
            role_id = int(op.attr(RPC_OP_ROLE_ATTR_NAME))
            if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or role_id == int(
                LR_SCHED_OP_ROLE_ATTR_VALUE
            ) | int(OPT_OP_ROLE_ATTR_VALUE):
                if self.sync_mode is False and op.type == 'increment':
                    inputs = self._get_input_map_from_op(
                        self.origin_program.global_block().vars, op
                    )
                    outputs = self._get_output_map_from_op(
                        self.origin_program.global_block().vars, op
                    )
                    for key in outputs:
                        counter_var = outputs[key]
                    all_trainer_counter_inputs = [
                        self.origin_program.global_block().create_var(
                            name=f"{counter_var.name}.trainer_{id_}",
                            type=counter_var.type,
                            shape=counter_var.shape,
                            dtype=counter_var.dtype,
                            persistable=counter_var.persistable,
                        )
                        for id_ in range(self.trainer_num)
                    ]
                    for i, op in enumerate(
                        self.startup_program.global_block().ops
                    ):
                        if op.type == 'fill_constant':
                            for key in op.output_names:
                                if (
                                    len(op.output(key)) == 1
                                    and op.output(key)[0] == counter_var.name
                                ):
                                    self.startup_program.global_block().ops[
                                        i
                                    ]._set_attr(
                                        'value', float(0.0 - self.trainer_num)
                                    )
                    for var in all_trainer_counter_inputs:
                        if (
                            var.name
                            == f"{counter_var.name}.trainer_{self.trainer_id}"
                        ):
                            self.counter_var = var
                        self.startup_program.global_block().create_var(
                            name=var.name,
                            type=var.type,
                            dtype=var.dtype,
                            shape=var.shape,
                            persistable=var.persistable,
                            initializer=Constant(1),
                        )
                    op_role_attr_name = (
                        core.op_proto_and_checker_maker.kOpRoleAttrName()
                    )
                    block._remove_op(index)
                    op = block._insert_op(
                        index,
                        type='sum',
                        inputs={'X': all_trainer_counter_inputs},
                        outputs=outputs,
                        attrs={op_role_attr_name: LR_SCHED_OP_ROLE_ATTR_VALUE},
                    )
                lr_ops.append(op)
                log("append lr op: ", op.type)
        return lr_ops

    def _get_lr_ops_deprecated(self):
        from paddle.distributed.transpiler.details import UnionFind

        lr_ops = []
        # find learning rate variables by optimize op
        lr_vars = set()
        for op in self.optimize_ops:
            if self._is_optimizer_op(op):
                lr_vars.add(op.input("LearningRate")[0])

        find_ops = []
        # find ops which output is lr var
        block = self.origin_program.global_block()
        for op in block.ops:
            if set(op.output_arg_names) & lr_vars:
                find_ops.append(op)
        # make a union find struct by the ops in default_main_program
        ufind = UnionFind(block.ops)

        for op1 in block.ops:
            for op2 in block.ops:
                # NOTE: we need to skip all optimize ops, since it is connected
                # with forward/backward ops and lr ops, we only need the lr ops.
                if (
                    op1 != op2
                    and self._is_op_connected(op1, op2)
                    and not self._is_optimizer_op(op1)
                    and not self._is_optimizer_op(op2)
                ):
                    ufind.union(op1, op2)
        # find all ops which is related with lr var
        for op1 in block.ops:
            for op2 in find_ops:
                if ufind.is_connected(op1, op2):
                    lr_ops.append(op1)
                    # we only need to append op for once
                    break
        return lr_ops

    def _is_opt_role_op(self, op):
        # NOTE: depend on oprole to find out whether this op is for
        # optimize
        op_maker = core.op_proto_and_checker_maker
        optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
        if op_maker.kOpRoleAttrName() in op.attr_names and int(
            op.all_attrs()[op_maker.kOpRoleAttrName()]
        ) == int(optimize_role):
            return True
        return False

    def _get_optimize_pass(self):
        """
        Get optimizer operators, parameters and gradients from origin_program
        Returns:
            opt_ops (list): optimize operators.
            params_grads (dict): parameter->gradient.
        """
        block = self.origin_program.global_block()
        opt_ops = []
        params_grads = []
        # tmp set to dedup
        optimize_params = set()
        origin_var_dict = self.origin_program.global_block().vars
        for op in block.ops:
            if self._is_opt_role_op(op):
                # Todo(chengmo): Whether clip related op belongs to Optimize guard should be discussed
                # delete clip op from opt_ops when run in Parameter Server mode
                if (
                    OP_NAME_SCOPE in op.all_attrs()
                    and CLIP_OP_NAME_SCOPE in op.attr(OP_NAME_SCOPE)
                    and self.config.mode != "nccl2"
                    and self.config.mode != "collective"
                ):
                    op._set_attr(
                        "op_role",
                        int(core.op_proto_and_checker_maker.OpRole.Backward),
                    )
                    continue
                opt_ops.append(op)
                if op.attr(OP_ROLE_VAR_ATTR_NAME):
                    param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
                    grad_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
                    if param_name not in optimize_params:
                        optimize_params.add(param_name)
                        log("adding param_grad pair: ", param_name, grad_name)
                        params_grads.append(
                            [
                                origin_var_dict[param_name],
                                origin_var_dict[grad_name],
                            ]
                        )
            else:
                pass

        # designed for special situation
        special_distribute_update_vars = self._get_distribute_update_vars()
        if special_distribute_update_vars:
            params_grads = params_grads + special_distribute_update_vars

        return opt_ops, params_grads

    def _get_distribute_update_vars(self):
        # TODO(chengmo): find more powerful and simple way to deal with these special situation
        """
        This Function is used for a special model, like PyramidDnn which has pyramid hash op.
        Some Parameters don't use optimizing op to update its value, but updated in its BP process.
        In these cases, Transpilse can't find these special vars by optimizing op information.
        So we add this function and add attr "distribute_update_vars" to tell transpiler these Parameter
        need to be updated in distribute training.
        We assume these special var send and receive the same var_name.
        """
        block = self.origin_program.global_block()
        origin_var_dict = self.origin_program.global_block().vars
        params = []
        for op in block.ops:
            special_attr = "distribute_update_vars"
            if special_attr in op.all_attrs():
                if op.attr(special_attr):
                    for param_name in op.attr(special_attr).split(","):
                        params.append(origin_var_dict[param_name])
        unique_params = list(set(params))
        params_grads = []
        for var in unique_params:
            params_grads.append([var, var])
        return params_grads