utils.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License

import copy
import logging
import os
import threading
import warnings
from functools import reduce

import numpy as np

import paddle
from paddle.fluid.framework import Variable
from paddle.fluid.io import is_belong_to_optimizer, is_parameter
from paddle.framework import core

from .dist_attribute import OperatorDistAttr, TensorDistAttr
from .process_group import get_all_process_groups
from .process_mesh import ProcessMesh

OpRole = core.op_proto_and_checker_maker.OpRole
OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()

__no_shape_var_type__ = [
    core.VarDesc.VarType.READER,
    core.VarDesc.VarType.STEP_SCOPES,
    core.VarDesc.VarType.LOD_TENSOR_ARRAY,
    core.VarDesc.VarType.FEED_MINIBATCH,
    core.VarDesc.VarType.FETCH_LIST,
]

__not_naive_data_parallel_op__ = ["expand_v2"]


def get_logger(log_level, name="auto_parallel"):
    logger = logging.getLogger(name)
    logger.propagate = False
    if not logger.handlers:
        logger.setLevel(log_level)
        log_handler = logging.StreamHandler()
        log_format = logging.Formatter(
            '%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s'
        )
        log_handler.setFormatter(log_format)
        logger.addHandler(log_handler)
    return logger


def is_valid_list_index(list, index):
    if index >= -len(list) and index < len(list):
        return True
    else:
        return False


def is_dim_shard(mapping):
    if mapping != -1:
        return True
    else:
        return False


def is_dim_replicate(mapping):
    if mapping == -1:
        return True
    else:
        return False


def verify_dims_mapping(dims_mapping, process_mesh):
    if dims_mapping is None:
        return False
    if not all(isinstance(d, int) for d in dims_mapping):
        return False
    for i in range(len(dims_mapping)):
        if dims_mapping[i] < -1 or dims_mapping[i] >= len(process_mesh.shape):
            return False
    for i in range(len(process_mesh.shape)):
        if dims_mapping.count(i) > 1:
            return False
    return True


def convert_to_dims_mapping(shard_spec, process_mesh):
    dims_mapping = []
    for shard in shard_spec:
        if shard is None:
            dims_mapping.append(-1)
        elif process_mesh.shape[process_mesh.dim_names.index(shard)] == 1:
            dims_mapping.append(-1)
        else:
            dims_mapping.append(process_mesh.dim_names.index(shard))
    return dims_mapping


def convert_to_shard_spec(dims_mapping, process_mesh):
    shard_spec = []
    for dim_mapping in dims_mapping:
        if dim_mapping == -1:
            shard_spec.append(None)
        else:
            shard_spec.append(process_mesh.dim_names[dim_mapping])
    return shard_spec


def verify_shard_spec(shard_spec, tensor_shape, process_mesh):
    if len(shard_spec) != len(tensor_shape):
        return False
    for shard in shard_spec:
        if shard is not None and not isinstance(shard, str):
            return False
        if shard is not None and shard not in process_mesh.dim_names:
            return False
    dims_mapping = convert_to_dims_mapping(shard_spec, process_mesh)
    if not verify_dims_mapping(dims_mapping, process_mesh):
        return False
    for i in range(len(tensor_shape)):
        if (
            dims_mapping[i] != -1
            and tensor_shape[i] > 0
            and tensor_shape[i] % process_mesh.shape[dims_mapping[i]] != 0
        ):
            return False
    return True


def compute_compatible_dim_mapping(dim_mappings):
    if not dim_mappings:
        return None
    compatible_mapping = dim_mappings[0]
    for mapping in dim_mappings:
        if compatible_mapping == -1:
            compatible_mapping = mapping
        elif mapping == -1:
            continue
        elif compatible_mapping == mapping:
            continue
        else:
            return None
    return compatible_mapping


def compute_compatible_dims_mapping(dims_mapping_list):
    if not dims_mapping_list:
        return None
    length = len(dims_mapping_list[0])
    for dims_mapping in dims_mapping_list:
        assert (
            dims_mapping is not None
        ), "Dims mapping must not be None for compatible computation"
        assert (
            len(dims_mapping) == length
        ), "The length of dims_mapping in list must be same for compatible computation."
    compatible_result = []
    for dim_mappings in zip(*dims_mapping_list):
        compatible_dim_mapping = compute_compatible_dim_mapping(
            list(dim_mappings)
        )
        if compatible_dim_mapping is None:
            return None
        compatible_result.append(compatible_dim_mapping)
    return compatible_result


def compute_compatible_process_mesh(process_mesh_list):
    compatible_process_mesh = None
    if not process_mesh_list:
        return compatible_process_mesh
    for process_mesh in process_mesh_list:
        if process_mesh is not None:
            if (
                compatible_process_mesh is None
                or compatible_process_mesh == process_mesh
            ):
                compatible_process_mesh = process_mesh
            else:
                return None
    return compatible_process_mesh


def compute_compatible_and_update_dim_mapping(dims_mapping_list, index_list):
    assert len(dims_mapping_list) == len(index_list)
    changed = False
    dim_mappings = []
    for i in range(len(dims_mapping_list)):
        assert is_valid_list_index(dims_mapping_list[i], index_list[i])
        dim_mappings.append(dims_mapping_list[i][index_list[i]])
    compatible_dim_mapping = compute_compatible_dim_mapping(dim_mappings)
    if compatible_dim_mapping is None:
        return False
    for i in range(len(dims_mapping_list)):
        if compatible_dim_mapping != dims_mapping_list[i][index_list[i]]:
            dims_mapping_list[i][index_list[i]] = compatible_dim_mapping
            changed = True
    return changed


def append_distributed_attr_suffix(name):
    """
    Append auto parallel suffix for distributed attribute name.
    """
    return name + core.kAutoParallelSuffix()


def remove_distributed_attr_suffix(name):
    """
    Remove auto parallel suffix from distributed attribute name.
    """
    return name.strip(core.kAutoParallelSuffix())


def check_distributed_attr_for_program(program, dist_context=None):
    from .dist_context import get_default_distributed_context

    if dist_context is None:
        dist_context = get_default_distributed_context()
    assert (
        dist_context.is_initialized_for_program()
    ), "Distributed attributes must be initialized before check."
    for block in program.blocks:
        for tensor in block.vars.values():
            dist_tensor = dist_context.get_dist_tensor_for_graph(tensor)
            tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
                tensor
            )
            if (tensor_dist_attr is not None) and (not dist_tensor.is_valid()):
                return False
        for op in block.ops:
            dist_op = dist_context.get_dist_op_for_graph(tensor)
            op_dist_attr = dist_context.get_op_dist_attr_for_program(op)
            if (op_dist_attr is not None) and (not dist_op.is_valid()):
                return False
    return True


def print_program_with_dist_attr(program, dist_context=None):
    """
    This function reuses the original program output ability with a distributed context.
    Using lock can avoid multiple threads change the default distributed context simultaneously.
    """
    lock = threading.Lock()
    lock.acquire()
    from .dist_context import (
        get_default_distributed_context,
        set_default_distributed_context,
    )

    if dist_context is None:
        dist_context = get_default_distributed_context()
        print(program, flush=True)
    else:
        original_default_context = get_default_distributed_context()
        set_default_distributed_context(dist_context)
        print(program, flush=True)
        set_default_distributed_context(original_default_context)
    lock.release()


def _get_comm_group(processes, shape, axis, rank):
    """
    Given a rank and the processes mesh the rank belongs to,
    compute the communication peers of the rank based on the give axis in the mesh.

    Example: 16 processes managed in a 4-Dimensinal mesh with shape of [2, 2, 2, 2].
    the rank communication peers of rank 0 (included) are following:
    in axis 0: [0, 1]
    in axis 1: [0, 2]
    in axis 2: [0, 4]
    in axis 3: [0, 8]
    """

    # NOTE _linear_idx2coordinate assume processes mesh start with 0 and continuous
    # tricks to support processes mesh when it is not start with 0 or continuous
    assert rank in processes, "rank [{}] is NOT in processes group {}".format(
        rank, processes
    )
    rank_relatvie = processes.index(rank)
    coordinate = _linear_idx2coordinate(shape, rank_relatvie)
    coordinates_in_group = [coordinate[:] for i in range(shape[axis])]

    # select comm group
    for i in range(shape[axis]):
        coordinates_in_group[i][axis] = i

    ranks_in_group_relative = [
        _coordinate2linear_idx(shape, coordinate)
        for coordinate in coordinates_in_group
    ]
    ranks_in_group = [processes[idx] for idx in ranks_in_group_relative]

    return sorted(ranks_in_group)


def _get_idx_in_axis(processes, shape, axis, rank):
    """
    Given a rank and the processes mesh the rank belongs to,
    compute the index of the rank in given axis.

    Example: 27 processes managed in a 3-Dimensinal mesh with shape of [3, 3, 3].
    the index of rank 22 are:
    in axis 0: 1
    in axis 1: 1
    in axis 2: 2
    """

    # NOTE _linear_idx2coordinate assume processes mesh start with 0 and continuous
    #  tricks to support processes mesh when it is not start with 0 or continuous
    rank_relatvie = processes.index(rank)
    coordinate = _linear_idx2coordinate(shape, rank_relatvie)
    return coordinate[axis]


def _coordinate2linear_idx(mesh_shape, coordinate):
    """
    convert a coordinate in multidimensional mesh space into a scala idx in linear space.

    it use Row-major order for dimension conversion.
    so it has:  [most_significant_dim, ..., least_significant_dim]
    assume:

        the size of i-th dimension to be:  S[i]
        the index of j-th dimension is: I[j]

    linear_idx of a n dimensional coordinate is:

        I[n-1] * (S[n-2] * S[n-3] * S[n-4] *     ....    S[0]) +
        I[n-2] * (         S[n-3] * S[n-4] *     ....    S[0]) +
        I[n-3] * (                  S[n-4] *     ....    S[0]) +
        ...
        I[1]   * (                                       S[0]) +
        I[0]

    """
    # NOTE the following function work based on a strong an assumption
    # that the processes in mesh are
    #    1. starts from 0
    #    2. continuous
    # it will be wrong if ths above condition doesnot meet,
    # e.g. process_mesh = { process_groups = [7, 8, 9,10, 12, 13, 14, 15], mesh = [2, 4]}
    # if you want a more general mapping, you should use cartesian product

    assert len(mesh_shape) == len(
        coordinate
    ), "coordinate should have the same size as mesh shape, but got shape: {}, coordinate: {}".format(
        mesh_shape, coordinate
    )
    for i in range(len(mesh_shape)):
        assert (
            coordinate[i] >= 0
        ), "index in dimension [{}] is least than zero. coordinate: {}".format(
            i, coordinate
        )
        assert (
            coordinate[i] < mesh_shape[i]
        ), "index beyond extent in dimension [{}]. shape: {}, coordinate: {}".format(
            i, mesh_shape, coordinate
        )

    base = mesh_shape[-1]
    linear_idx = coordinate[-1]

    # row major order
    for i in range(len(mesh_shape) - 2, -1, -1):
        linear_idx += base * coordinate[i]
        base *= mesh_shape[i]

    return linear_idx


def _linear_idx2coordinate(mesh_shape, linear_idx):
    """
    mapping a linear scala into multidimensional mesh space, return it coordinate in that space.

    it is the inverse function of _coordinate2linear_idx.
    assume:

        the size of i-th dimension to be:  S[i]
        the index of j-th dimension is: I[j]

    the coordinate given linear_idx is:

        I[0] = linear_idx                                  % S[0]
        I[0] = (linear_idx / S[0])                         % S[1]
        I[0] = (linear_idx / (S[0] * S[1]))                % S[2]
        ....

    """

    assert linear_idx >= 0, "linear index [{}] is least than zero".format(
        linear_idx
    )
    assert linear_idx < np.prod(
        mesh_shape
    ), "linear index beyond the extent of mesh shape. shape: {}, linear index: {}".format(
        mesh_shape, linear_idx
    )

    base = 1
    coordinate = [-1] * len(mesh_shape)

    for i in reversed(range(len(mesh_shape))):
        offset = linear_idx / base
        coordinate[i] = int(offset % mesh_shape[i])
        base *= mesh_shape[i]

    # row major order
    return coordinate


def _get_corresponding_rank(dist_context, target_mesh, rank):

    # TODO(JZ-LIANG) a hack method to support varying mesh in Pipeline parallelism case.
    # we assume that all mesh are evenly divide from a parent mesh and should have same size.
    # to revise this in future.

    coordinate = None
    for mesh in dist_context.process_meshes:
        if rank in mesh.process_ids and mesh.shape == target_mesh.shape:
            coordinate = _linear_idx2coordinate(
                mesh.shape, mesh.process_ids.index(rank)
            )
            break

    # assert coordinate is not None, "could NOT found rank [{}] in any registered mesh".format(
    #     rank)
    if coordinate is not None:
        return target_mesh.process_ids[
            _coordinate2linear_idx(mesh.shape, coordinate)
        ]
    else:
        return target_mesh.process_ids[0]


def _get_unshard_dist_shape(var, dist_attr):
    var_shape = var.shape
    mapping = dist_attr.dims_mapping
    mesh = dist_attr.process_mesh.shape
    assert len(var_shape) == len(
        mapping
    ), "variable shape [{}] and dim_mapping [{}] is NOT match !".format(
        var_shape, mapping
    )
    new_shape = []
    for idx in range(len(var_shape)):
        if var_shape[idx] == -1 or mapping[idx] == -1:
            new_shape.append(var_shape[idx])
        else:
            new_shape.append(var_shape[idx] * mesh[mapping[idx]])

    return new_shape


def make_data_unshard(dist_main_prog, dist_startup_prog, dist_context=None):
    from .dist_context import get_default_distributed_context

    if dist_context is None:
        dist_context = get_default_distributed_context()

    for var in dist_main_prog.list_vars():
        if var.is_data:
            tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
                var
            )
            inverse_shape = _get_unshard_dist_shape(var, tensor_dist_attr)
            var.desc.set_shape(inverse_shape)
            dim_mapping = tensor_dist_attr.dims_mapping
            dim_mapping = [-1] * len(dim_mapping)
            tensor_dist_attr.dims_mapping = dim_mapping
            dist_context.set_tensor_dist_attr_for_program(var, tensor_dist_attr)


def _update_addition_info(addition_info):
    """Update default addition_info with inputs"""
    add_info = {"epoch": 0, "batch": 0, "batch_size": 0}
    if not addition_info:
        return add_info
    elif not isinstance(addition_info, dict):
        raise TypeError(
            "The type of 'addition_info' should be 'dict', "
            "but got '{}'.".format(str(type(addition_info)))
        )
    else:
        for item, value in addition_info.items():
            if item not in ["epoch", "batch", "batch_size"]:
                raise ValueError(
                    "The key of 'addition_info' should be one of the "
                    "['epoch', 'batch', 'batch_size'], but got '{}'.".format(
                        str(item)
                    )
                )
            if not isinstance(value, int):
                raise ValueError(
                    "The value of 'addition_info' should be 'int', "
                    "but got '{}'.".format(str(type(value)))
                )
            add_info[item] = value
        return add_info


def _check_valid_path(file_path):
    """Validity check of input file path"""
    if not file_path:
        return file_path
    elif isinstance(file_path, list):
        for file in file_path:
            if not isinstance(file, str):
                raise TypeError(
                    "The type of file path should be 'str', "
                    "but got '{}'.".format(str(type(file)))
                )
            if not os.path.exists(file):
                raise ValueError(
                    "The file path '{}' does not exist.".format(file)
                )
        return file_path
    else:
        raise TypeError(
            "The type of file path should be 'list', "
            "but got '{}'.".format(str(type(file_path)))
        )


def _check_param_dict(param_dict):
    if not param_dict:
        raise ValueError("'param_dict' cannot be None.")
    elif not isinstance(param_dict, dict):
        raise TypeError(
            "The type of 'param_dict' should be 'dict', "
            "but got '{}'.".format(str(type(param_dict)))
        )
    else:
        for name, value in param_dict.items():
            if not isinstance(name, str):
                raise TypeError(
                    "The type of key of 'param_dict' should be 'str', "
                    "but got '{}'.".format(str(type(name)))
                )
            if not isinstance(value, paddle.fluid.LoDTensor):
                raise TypeError(
                    "The type of value of 'param_dict' should be 'LoDTensor', "
                    "but got '{}'.".format(str(type(value)))
                )
        return param_dict


def _check_dist_attr(dist_attr):
    if not dist_attr:
        return dist_attr
    elif not isinstance(dist_attr, dict):
        raise TypeError(
            "The type of 'dist_attr' should be 'dict', "
            "but got '{}'.".format(str(type(dist_attr)))
        )
    else:
        for name, value in dist_attr.items():
            if not isinstance(name, str):
                raise TypeError(
                    "The type of param name of 'dist_attr' should be 'str', "
                    "but got '{}'.".format(str(type(name)))
                )
            if not isinstance(value, dict):
                raise TypeError(
                    "The type of distributed attribute should be 'dict', "
                    "but got '{}'".format(str(type(value)))
                )
            attr = ['process_shape', 'process_group', 'dims_mapping']
            if list(value.keys()) != attr:
                raise ValueError(
                    "The key of distributed attribute should be "
                    "'['process_shape', 'process_group', 'dims_mapping']', "
                    "but got {}.".format(str(value.keys()))
                )
        return dist_attr


def save_distributed_checkpoint(
    program,
    checkpoint_path,
    dist_attr_path,
    addition_info=None,
    is_integrated=False,
    dist_context=None,
):
    """
    Save model parameter state, optimzer state, distributed attribute and
    additional information of each rank.

    Args:
        program(Program): The program to be saved.
        checkpoint_path(str): The path of the checkpoint file to be saved.
        dist_attr_path(str): The path of distributed attribute file to be saved.
        addition_info(dict, optional): Additional information, key should be selected in ['epoch', 'batch', 'batch_size'].
            Default values are 0, when 'addition_info' is None. Default: None.
        is_integrated(bool, optional): Whether to integrate param before save. Default: False.
        dist_context(DistributedContext ,optional): collect related distributed information for program

    Returns:
        None

    Examples:
        .. code-block:: python

            path = os.path.join("./output", "step_%d" % step)
            os.makedirs(path, exist_ok=True)
            add_info = {'batch': step, "batch_size": global_batch_size}
            save_distributed_checkpoint(program, path, path, add_info)
    """
    from .dist_context import get_default_distributed_context

    assert isinstance(program, paddle.fluid.framework.Program)
    assert isinstance(is_integrated, bool)
    if dist_context is None:
        dist_context = get_default_distributed_context()
    addition_info = _update_addition_info(addition_info)

    if not is_integrated:
        _save_distributed_state_dict(program, addition_info, checkpoint_path)
        _save_distributed_attribute(program, dist_attr_path, dist_context)
    else:
        # TODO: integrate param before save
        raise NotImplementedError(
            "Integrating parameter has not been implemented."
        )


def load_distributed_checkpoint(checkpoint_path, dist_attr_path):
    """
    Load parameter, optimizer, distributed attribute and addition_info.

    Args:
        checkpoint_path(list[str]): model parameter file path, must be in order of rank id.
        dist_attr_path(list[str]): distributed attribute file path, must be in order of rank id.

    Returns:
        param_dict(dict): parameters' value of all ranks.
        dist_attr(dict): parameters' distributed attribute.
        addition_info(dict): additional information user saved in last training.

    Notes:
        The return, 'addition_info', is belonging to the first file of checkpoint_path by default.

    Examples:
        .. code-block:: python

            ckpt_path = ['./model_state_rank0.pdmodel',
                         './model_state_rank1.pdmodel']
            dist_attr_path = ['./dist_attr_rank0.pdattr',
                              './dist_attr_rank1.pdattr']
            param_dict, dist_attr, add_info = load_distributed_checkpoint(ckpt_path, dist_attr_path)
    """
    assert _check_valid_path(
        checkpoint_path
    ), "'checkpoint_path' cannot be None."
    assert _check_valid_path(dist_attr_path), "'dist_attr_path' cannot be None."

    state_dict_info = _load_distributed_state_dict(checkpoint_path)
    dist_attr = _load_distributed_attribute(dist_attr_path)
    param_dict = state_dict_info["model"]
    addition_info = state_dict_info["addition_info"]
    return param_dict, dist_attr, addition_info


def load_checkpoint_into_program(
    checkpoint_path, dist_attr_path, program, dist_context=None
):
    """
    Load parameter, optimizer, distributed attribute and addition_info into model.

    Args:
        checkpoint_path(list[str]): model parameter file path, must be in order of rank id.
        dist_attr_path(list[str]): distributed attribute file path, must be in order of rank id.
        program(Program): the program to be updated with checkpoint_path.
        dist_context(DistributedContext ,optional): collect related distributed information for program

    Returns:
        addition_info(dict): user saved in last train.

    Notes:
        The return, 'addition_info', is belonging to the first file of checkpoint_path by default.

    Examples:
        .. code-block:: python

            exe.run(startup_program)
            ckpt_path = ['./model_state_rank0.pdmodel',
                         './model_state_rank1.pdmodel']
            dist_attr_path = ['./dist_attr_rank0.pdattr',
                              './dist_attr_rank1.pdattr']
            load_checkpoint_into_program(ckpt_path, dist_attr_path, main_program)
    """
    from .dist_context import get_default_distributed_context

    assert isinstance(program, paddle.fluid.framework.Program)
    assert _check_valid_path(
        checkpoint_path
    ), "'checkpoint_path' cannot be None."
    assert _check_valid_path(dist_attr_path), "'dist_attr_path' cannot be None."
    if dist_context is None:
        dist_context = get_default_distributed_context()
    all_state_dict_info = _load_distributed_state_dict(checkpoint_path)
    all_pre_dist_attr = _load_distributed_attribute(dist_attr_path)
    all_cur_dist_attr = get_dist_attr(program, dist_context)
    all_param_dict = all_state_dict_info["model"]
    addition_info = all_state_dict_info["addition_info"]
    sliced_param_dict = merge_and_slice_parameter(
        all_param_dict, all_pre_dist_attr, all_cur_dist_attr
    )
    load_parameter_into_program(sliced_param_dict, program)

    return addition_info


def load_parameter_into_program(param_dict, program):
    """
    Load parameters into program.

    Args:
        param_dict(dict): parameters' name and value.
        program(Program): the program to be updated
    """
    assert isinstance(param_dict, dict)
    assert program and isinstance(program, paddle.fluid.framework.Program)
    if not param_dict:
        return
    program.set_state_dict(param_dict)


def _save_distributed_attribute(program, dist_attr_path, dist_context):
    """Save distributed attribute of all parameters"""
    # TODO: just save a complete distributed attribute file
    rank_id = paddle.distributed.get_rank()
    dist_attr_name = os.path.join(
        dist_attr_path, "dist_attr_rank{}.pdattr".format(rank_id)
    )
    dist_attr_dict = {
        "model": get_dist_attr(program, dist_context),
        "world_size": paddle.distributed.get_world_size(),
    }
    paddle.save(dist_attr_dict, dist_attr_name)
    logging.info(
        "Already saved distributed attribute to '{}'.".format(dist_attr_path)
    )


def _load_distributed_attribute(dist_attr_path):
    """Load parameters' distributed attribute from dist_attr_path"""
    total_dist_attr = {}
    for dist_attr_file in dist_attr_path:
        dist_attr = paddle.load(dist_attr_file)
        pre_world_size = dist_attr["world_size"]
        assert pre_world_size == len(
            dist_attr_path
        ), "The number of 'dist_attr_path' must be equal to the last training world size."
        for name, attr in dist_attr["model"].items():
            if name not in total_dist_attr:
                total_dist_attr[name] = attr

    return total_dist_attr


def _save_distributed_state_dict(program, addition_info, checkpoint_path):
    """Save parameters' state_dict"""
    rank = paddle.distributed.get_rank()
    ckpt_file_name = os.path.join(
        checkpoint_path, "model_state_rank{}.pdmodel".format(rank)
    )
    state_dict = {
        "model": program.state_dict(),
        "world_size": paddle.distributed.get_world_size(),
        "addition_info": addition_info,
    }
    paddle.save(state_dict, ckpt_file_name)
    logging.info("Already saved model to '{}'.".format(checkpoint_path))


def _load_distributed_state_dict(checkpoint_path):
    """Load parameters' state_dict from checkpoint_path"""
    all_state_dict = {}
    for idx, ckpt_file in enumerate(checkpoint_path):
        state_dict_info = paddle.load(ckpt_file, return_numpy=True)
        pre_world_size = state_dict_info["world_size"]
        assert pre_world_size == len(
            checkpoint_path
        ), "The number of 'checkpoint_path' must be equal to the last training world size."
        if idx == 0:
            addition_info = state_dict_info["addition_info"]
        for name, value in state_dict_info["model"].items():
            if name in all_state_dict:
                all_state_dict[name].append(np.array(value))
            else:
                all_state_dict[name] = [np.array(value)]

    all_state_dict_info = {
        "model": all_state_dict,
        "addition_info": addition_info,
    }
    return all_state_dict_info


def get_dist_attr(program, dist_context=None):
    """
    Get distributed attribute of current rank.

    Args:
        program(Program): main program for training
    """
    from .dist_context import get_default_distributed_context

    assert isinstance(program, paddle.fluid.framework.Program)
    if dist_context is None:
        dist_context = get_default_distributed_context()
    dist_attr = {}
    for var in program.list_vars():
        if is_parameter(var) or is_belong_to_optimizer(var):
            tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(
                var
            )
            process_mesh = tensor_dist_attr.process_mesh
            dims_mapping = tensor_dist_attr.dims_mapping
            dist_attr[var.name] = {
                "process_shape": process_mesh.shape,
                "process_group": process_mesh.process_ids,
                "dims_mapping": dims_mapping,
            }
    return dist_attr


def merge_and_slice_parameter(dist_param_dict, pre_dist_attr, cur_dist_attr):
    """
    Merge parameters with previous dist_attr and slice parameters with current dist_attr

    Arags:
        dist_param_dict(dict): parameters' value of all ranks.
        pre_dist_attr(dict): parameters' dist_attr of last training process.
        cur_dist_attr(dict): parameters' dist_attr of current training process.

    Returns:
        dist_param_dict(dict): parameters' value of current rank.
    """
    assert _check_dist_attr(pre_dist_attr), "'pre_dist_attr' cannot be None."
    assert isinstance(
        dist_param_dict, dict
    ), "The type of 'dist_param_dict' should be 'dict', but got {}.".format(
        str(type(dist_param_dict))
    )
    for name, value in dist_param_dict.items():
        if not isinstance(name, str):
            raise TypeError(
                "The key of 'dist_param_dict' is parameter's name, "
                "and its type should be 'str', but got {}.".format(
                    str(type(name))
                )
            )
        if not isinstance(value, list) or not all(
            isinstance(v, np.ndarray) for v in value
        ):
            raise TypeError(
                "The value of 'dist_param_dict' is parameter's value of all ranks, "
                "and its type should be 'list(numpy.ndarray)'."
            )

    if cur_dist_attr is None:
        return {}

    param_not_in_pre = []
    param_not_in_cur = []
    logging.info("Start to merge and slice parameters.")
    for var_name in cur_dist_attr.keys():
        if var_name not in pre_dist_attr:
            param_not_in_pre.append(var_name)
            continue

        pre_attr = pre_dist_attr[var_name]
        cur_attr = cur_dist_attr[var_name]
        if pre_attr == cur_attr:
            # skip merge and slice
            rank_id = paddle.distributed.get_rank()
            index = cur_attr["process_group"].index(rank_id)
            param = dist_param_dict[var_name][index]
            dist_param_dict[var_name] = param
            continue

        pre_param = dist_param_dict[var_name]
        pre_dims_mapping = pre_attr["dims_mapping"]
        cur_dims_mapping = cur_attr["dims_mapping"]
        if len(set(pre_dims_mapping)) > 1 or -1 not in pre_dims_mapping:
            complete_param = _merge_parameter_with_dist_attr(
                pre_param, pre_attr
            )
            dist_param_dict[var_name] = complete_param
        else:
            complete_param = pre_param[0]
            dist_param_dict[var_name] = complete_param

        if len(set(cur_dims_mapping)) > 1 or -1 not in cur_dims_mapping:
            sliced_param = _slice_parameter_with_dist_attr(
                complete_param, cur_attr
            )
            dist_param_dict[var_name] = sliced_param

    for var_name in pre_dist_attr:
        if var_name not in cur_dist_attr:
            param_not_in_cur.append(var_name)
            dist_param_dict.pop(var_name)

    if param_not_in_pre:
        warnings.warn(
            "Parameters '{}' are not found in last training process.".format(
                str(param_not_in_pre)
            )
        )
    if param_not_in_cur:
        warnings.warn(
            "Parameters '{}' are not found in current training process.".format(
                str(param_not_in_cur)
            )
        )

    return dist_param_dict


def _merge_parameter_with_dist_attr(param_list, dist_attr):
    """Merge parameter with distributed attribute"""
    from .reshard import Resharder

    dims_mapping = dist_attr["dims_mapping"]
    process_shape = dist_attr["process_shape"]
    process_group = dist_attr["process_group"]
    # get the complete shape of the parameter
    complete_shape = Resharder.compute_complete_shape(
        param_list[0].shape, process_shape, dims_mapping
    )
    # merge the parameter with dist_attr
    partition_param_list = []
    merged_partiton = []
    for process in process_group:
        partition_index = Resharder.compute_partition_index(
            process, complete_shape, dims_mapping, process_shape, process_group
        )
        index = process_group.index(process)
        if partition_index not in merged_partiton:
            merged_partiton.append(partition_index)
            _merge_parameter(
                partition_param_list,
                param_list[index],
                partition_index,
                complete_shape,
            )

    assert (
        len(partition_param_list) == 1 or not partition_param_list
    ), "Fail to merge parameter"
    complete_param = partition_param_list[0][0]
    return complete_param


def _slice_parameter_with_dist_attr(param, dist_attr):
    """Slice parameter with distributed attribute"""
    param = (
        np.array(param) if isinstance(param, paddle.fluid.LoDTensor) else param
    )
    dims_mapping = dist_attr["dims_mapping"]
    process_shape = dist_attr["process_shape"]
    process_group = dist_attr["process_group"]
    # slice the parameter with dist_attr
    partition_index_list = _get_split_indices(
        param.shape, dims_mapping, process_shape, process_group
    )
    sliced_param_list = _slice_parameter(
        param, partition_index_list, len(partition_index_list)
    )
    # get the current parameter's index in sliced_param_list
    rank_id = paddle.distributed.get_rank()
    sliced_param_index = _get_sliced_param_index(
        rank_id, param.shape, dims_mapping, process_shape, process_group
    )
    sliced_param = sliced_param_list[sliced_param_index]
    return sliced_param


def _merge_parameter(
    partition_param_list, param, partition_index, complete_shape
):
    """
    Merge partitial parameters to a complete one.

    Returns:
        None

    Examples:
        .. code-block:: python

            import numpy as np
            partition_param_list = [(np.array([[[1.11, 1.12]]]), [[0,1],[0,1],[0,2]])]
            param = np.array([[[1.13, 1.14]]])
            partition_index = [[0,1],[0,1],[2,4]]

            _merge_parameter(partition_param_list, param, partition_index)
            # partition_param_list: [(np.array([[[1.11, 1.12, 1.13, 1.14]]]), [[0,1],[0,1],[0,4]])]
    """
    from .reshard import Resharder

    if len(partition_param_list) == 1:
        is_complete_data = True
        for idx, item in enumerate(partition_param_list[0][1]):
            if item[0] != 0 or item[1] != complete_shape[idx]:
                is_complete_data = False
                break
        if is_complete_data:
            return

    if not partition_param_list:
        partition_param_list.append((param, partition_index))
    else:
        i = 0
        while i < len(partition_param_list):
            (
                concat_axis,
                first_order,
                new_partition,
            ) = Resharder.compute_concat_info(
                partition_param_list[i][1], partition_index
            )
            if concat_axis != -1:
                if first_order == 0:
                    new_param = np.concatenate(
                        (partition_param_list[i][0], param), axis=concat_axis
                    )
                else:
                    new_param = np.concatenate(
                        (param, partition_param_list[i][0]), axis=concat_axis
                    )

                partition_param_list.pop(i)
                _merge_parameter(
                    partition_param_list,
                    new_param,
                    new_partition,
                    complete_shape,
                )
                break
            i += 1


def _slice_parameter(complete_param, partition_index_list, length):
    """
    Slice a complete parameter.

    Returns:
        sliced_param_list(list): sliced parameters with 'partition_index_list'

    Examples:
        .. code-block:: python

            import numpy as np
            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
            rank = 2
            complete_shape = [1, 1, 6]
            dims_mapping = [-1, -1, 0]
            process_shape = [3]
            process_group = [0, 1, 2]

            sliced_param_list = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
            # [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
    """
    sliced_param_list = []
    axis = len(complete_param.shape) - length
    sliced_param = np.split(
        complete_param, partition_index_list[axis], axis=axis
    )
    if length == 1:
        return sliced_param
    for param in sliced_param:
        sliced_param_list.extend(
            _slice_parameter(param, partition_index_list, length - 1)
        )
    return sliced_param_list


def _get_sliced_param_index(
    rank, complete_shape, dims_mapping, process_shape, process_group
):
    """
    Get sliced_param's index of current rank in all sliced parameters list.

    Returns:
        sliced_param_index(int): the index of sliced param in sliced_param_list

    Examples:
        .. code-block:: python

            import numpy as np
            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
            rank = 2
            complete_shape = [1, 1, 6]
            dims_mapping = [-1, -1, 0]
            process_shape = [3]
            process_group = [0, 1, 2]

            slice_param = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
            # slice_param:
            # [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]

            index = _get_sliced_param_index(rank, complete_shape, dims_mapping
                                            process_shape, process_group)
            # index: 2
    """
    from .reshard import Resharder

    partition_index = Resharder.compute_partition_index(
        rank, complete_shape, dims_mapping, process_shape, process_group
    )
    sliced_param_index = 0
    for i, shape in enumerate(complete_shape):
        if dims_mapping[i] == -1:
            slice_shape = shape
        else:
            slice_shape = shape // process_shape[dims_mapping[i]]
        if slice_shape == 1:
            index = partition_index[i][0]
        else:
            index = (partition_index[i][0] + 1) // slice_shape
        sliced_param_index = sliced_param_index * (shape // slice_shape) + index
    return sliced_param_index


def _get_split_indices(
    complete_shape, dims_mapping, process_shape, process_group
):
    """
    Get split indices of every dimension.

    Returns:
        split_indices_list(list): the split indices of every dimension of the parameter

    Examples:
        .. code-block:: python

            import numpy as np
            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
            complete_shape = [1, 1, 6]
            dims_mapping = [-1, -1, 0]
            process_shape = [3]
            process_group = [0, 1, 2]

            index = _get_split_indices(complete_shape, dims_mapping, process_shape, process_group)
            # index: [[], [], [2, 4]]
    """
    from .reshard import Resharder

    split_indices_list = []
    for process in process_group:
        partition_index = Resharder.compute_partition_index(
            process, complete_shape, dims_mapping, process_shape, process_group
        )
        if split_indices_list:
            for dim in range(len(partition_index)):
                split_indices_list[dim].extend(partition_index[dim])
        else:
            split_indices_list = partition_index
    split_indices_list = list(
        map(
            lambda x, y: list(set(x) - set([y]) - set([0])),
            split_indices_list,
            complete_shape,
        )
    )
    split_indices_list = [sorted(x) for x in split_indices_list]
    return split_indices_list


def set_grad_var_shape(program, dist_context):
    from paddle.distributed.fleet.meta_optimizers.common import OpRole

    from .operators.common import infer_shape

    block = program.global_block()
    vars = block.vars
    appended_grad_times = 0
    grad_var_to_var = dist_context.dist_op_context.grad_var_to_var

    for idx, op in enumerate(block.ops):

        if int(op.attr('op_role')) != int(OpRole.Backward):
            continue

        if (
            int(block.ops[idx - 1].attr('op_role')) == int(OpRole.Forward)
            or int(block.ops[idx - 1].attr('op_role')) == 257
        ):
            appended_grad_times += 1

        if op.type in ["check_finite_and_unscale", "update_loss_scaling"]:
            break

        if op.type in ["sum", "concat", "shape"]:
            continue

        op_dist_attr = dist_context.get_op_dist_attr_for_program(op)
        assert op_dist_attr is not None

        for var_name in op.output_arg_names:

            if "@GRAD" not in var_name:
                continue
            if var_name in grad_var_to_var[appended_grad_times]:
                forward_var_name = grad_var_to_var[appended_grad_times][
                    var_name
                ]
            else:
                forward_var_name = var_name[: var_name.find("@GRAD")]

            if op.type in [
                "c_allreduce_sum",
                "c_identity",
                "scale",
                "cast",
                "fill_any_like",
            ]:
                forward_var_name = op.input_arg_names[0]
            elif (
                op.type == "matmul_v2_grad"
                or op.type == "matmul_grad"
                or op.type == "mul_grad"
            ):
                forward_var_name = None
                for output_name in op.output_names:
                    if var_name in op.output(output_name):
                        assert "@GRAD" in output_name
                        input_name = output_name[: output_name.find("@GRAD")]
                        assert len(op.input(input_name)) == 1
                        forward_var_name = op.input(input_name)[0]
                assert forward_var_name is not None

            need_set_shape_list = [
                "reshape2_grad",
                "softmax_with_cross_entropy_grad",
                "transpose2_grad",
                "softmax_grad",
                "cross_entropy_grad2",
                "dropout_grad",
                "tanh_grad",
                "slice",
                "assign",
                "matmul_v2_triple_grad",
                "elementwise_add_triple_grad",
                "fill_constant",
                "sqrt_grad",
                "fused_softmax_mask_upper_triangle_grad",
                "flatten_contiguous_range_grad",
                "relu_grad",
                "exp_grad",
                "sigmoid_grad",
            ]
            forward_list = [
                "reshape2",
                "softmax_with_cross_entropy",
                "transpose2",
                "softmax",
                "cross_entropy2",
                "dropout",
                "tanh",
                ["slice_grad", "c_allgather"],
                "assign",
                "matmul_v2_grad_grad",
                "elementwise_add_grad_grad",
                "shape",
                "sqrt",
                "fused_softmax_mask_upper_triangle",
                "flatten_contiguous_range",
                "relu",
                "exp",
                "sigmoid",
            ]
            if op.type in need_set_shape_list:
                for forward_op in block.ops:
                    idx = need_set_shape_list.index(op.type)
                    forward_op_name = forward_list[idx]
                    if (
                        forward_op.type in forward_op_name
                        and forward_var_name in forward_op.input_arg_names
                    ):
                        op_dist_attr = (
                            dist_context.get_op_dist_attr_for_program(
                                forward_op
                            )
                        )
                        break

            forward_input_dist_attr = op_dist_attr.get_input_dist_attr(
                forward_var_name
            )
            assert (
                forward_input_dist_attr is not None
            ), f"{forward_var_name, str(op)}"
            forward_var = vars[forward_var_name]
            forward_var_dist_attr = (
                dist_context.get_tensor_dist_attr_for_program(forward_var)
            )
            assert forward_var_dist_attr is not None
            grad_var = vars[var_name]
            ref_shape = infer_shape(
                block,
                forward_var,
                forward_var_dist_attr,
                forward_input_dist_attr,
            )

            if list(grad_var.shape) != ref_shape:
                grad_var.desc.set_shape(ref_shape)


def is_forward_op(op):
    op_role = int(op.attr('op_role'))
    return OP_ROLE_KEY in op.attr_names and (
        op_role == int(OpRole.Forward) or op_role == int(OpRole.Loss)
    )


def is_backward_op(op):
    return OP_ROLE_KEY in op.attr_names and int(
        op.all_attrs()[OP_ROLE_KEY]
    ) & int(OpRole.Backward)


def is_optimize_op(op):
    return OP_ROLE_KEY in op.attr_names and int(
        op.all_attrs()[OP_ROLE_KEY]
    ) & int(OpRole.Optimize)


def is_lr_sched_op(op):
    return OP_ROLE_KEY in op.attr_names and int(
        op.all_attrs()[OP_ROLE_KEY]
    ) & int(OpRole.Optimize.LRSched)


def is_loss_op(op):
    return OP_ROLE_KEY in op.attr_names and int(
        op.all_attrs()[OP_ROLE_KEY]
    ) == (int(OpRole.Forward) | int(OpRole.Loss))


def is_loss_grad_op(op):
    if OP_ROLE_KEY not in op.attr_names:
        return False
    op_role = int(op.all_attrs()[OP_ROLE_KEY])
    return op_role & int(OpRole.Backward) and op_role & int(OpRole.Loss)


def is_gradient_clip_op(op):
    return op.desc.has_attr("op_namescope") and op.desc.attr(
        "op_namescope"
    ).startswith("/gradient_clip")


def is_prim_op(op):
    return op.type.endswith("_p")


def get_loss_op(block):
    loss_ops = []
    for op in block.ops:
        if is_loss_op(op):
            assert (
                len(op.desc.output_arg_names()) == 1
            ), "loss op should only output loss var"
            loss_ops.append(op)

    assert len(loss_ops) == 1, "num of loss op is not equal to one"
    return loss_ops[0]


def set_var_dist_attr(dist_context, var, dims_mapping, process_mesh, **kwargs):
    tensor_dist_attr = TensorDistAttr()
    tensor_dist_attr.dims_mapping = dims_mapping
    # TODO get global mesh group
    if isinstance(process_mesh, (list, np.ndarray)):
        tensor_dist_attr.process_mesh = ProcessMesh(process_mesh)
    elif isinstance(process_mesh, core.ProcessMesh):
        tensor_dist_attr.process_mesh = process_mesh
    else:
        raise ValueError(
            "{} must be a instance of ProcessMesh or list, but receive {}".format(
                process_mesh, type(process_mesh)
            )
        )
    if "mark_annotated" in kwargs and kwargs["mark_annotated"]:
        tensor_dist_attr.mark_annotated("dims_mapping")
        tensor_dist_attr.mark_annotated("process_mesh")
    dist_context.set_tensor_dist_attr_for_program(var, tensor_dist_attr)
    return tensor_dist_attr


def naive_set_dist_op_attr_for_program_by_mesh_and_mapping(
    new_op, process_mesh, ref_mapping, ctx
):
    assert process_mesh is not None
    assert ref_mapping is not None

    new_op_dist_attr = OperatorDistAttr()

    for input_varname in new_op.desc.input_arg_names():
        new_op_dist_attr.set_input_dims_mapping(input_varname, ref_mapping)
    for output_varname in new_op.desc.output_arg_names():
        new_op_dist_attr.set_output_dims_mapping(output_varname, ref_mapping)

    new_op_dist_attr.process_mesh = process_mesh
    ctx.set_op_dist_attr_for_program(new_op, new_op_dist_attr)


def naive_set_dist_op_attr_for_program_by_mesh(
    new_op, process_mesh, ctx, is_recompute=False
):
    # hack to skip coalesce var for dist attr
    if not is_recompute:
        return
    assert process_mesh is not None

    new_op_dist_attr = OperatorDistAttr()

    for input_varname in new_op.desc.input_arg_names():
        var = ctx.serial_main_program.global_block().var(input_varname)
        mapping = ctx.get_tensor_dist_attr_for_program(var).dims_mapping
        new_op_dist_attr.set_input_dims_mapping(input_varname, mapping)
    for output_varname in new_op.desc.output_arg_names():
        var = ctx.serial_main_program.global_block().var(output_varname)
        mapping = ctx.get_tensor_dist_attr_for_program(var).dims_mapping
        new_op_dist_attr.set_output_dims_mapping(output_varname, mapping)

    new_op_dist_attr.process_mesh = process_mesh
    new_op_dist_attr.is_recompute = is_recompute
    ctx.set_op_dist_attr_for_program(new_op, new_op_dist_attr)


def update_op_dims_mapping_by_default_dist_impl(dist_op):
    changed = False
    op_dist_attr = dist_op.dist_attr
    op_desc = dist_op.serial_op.desc
    # The following statement will be replaced by a more elegent way
    if op_desc.type() == "shape" or op_desc.type() == "slice":
        return False
    output_names = op_desc.output_names()
    xshape_arg_names = []
    if "XShape" in output_names:
        xshape_arg_names = op_desc.output("XShape")
    batch_dim_mappings = []
    for arg_name in op_desc.input_arg_names():
        serial_tensor = dist_op.get_serial_input(arg_name)
        if serial_tensor.is_parameter:
            continue
        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
        if len(dims_mapping) > 1:
            for idx, mapping in enumerate(dims_mapping[1:]):
                assert (
                    mapping == -1
                ), "{} only the batch dimension (0-dim) can be sharded, but the dimension {} is sharded by {} part.".format(
                    op_desc.type(), idx, mapping
                )
        batch_dim_mappings.append(dims_mapping[0])
    for arg_name in op_desc.output_arg_names():
        serial_tensor = dist_op.get_serial_output(arg_name)
        if serial_tensor.is_parameter:
            continue
        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
        if arg_name not in xshape_arg_names:
            if len(dims_mapping) > 1:
                for idx, mapping in enumerate(dims_mapping[1:]):
                    assert (
                        mapping == -1
                    ), "{} only the batch dimension (0-dim) can be sharded, but the dimension {} is sharded by {} part.".format(
                        op_desc.type(), idx, mapping
                    )
            batch_dim_mappings.append(dims_mapping[0])
        else:
            assert (
                dims_mapping[0] == -1
            ), "{} only the batch dimension (1-dim) of XShape can be sharded, but the dimension 0 is sharded by {} part.".format(
                op_desc.type(), mapping
            )
            if len(dims_mapping) > 2:
                for idx, mapping in enumerate(dims_mapping[2:]):
                    assert (
                        mapping == -1
                    ), "{} only the batch dimension (1-dim) of XShape can be sharded, but the dimension {} is sharded by {} part.".format(
                        op_desc.type(), idx, mapping
                    )
            batch_dim_mappings.append(dims_mapping[1])

    compatible_dim_mapping = compute_compatible_dim_mapping(batch_dim_mappings)
    assert (
        compatible_dim_mapping is not None
    ), "There is no compatible dim mapping."
    for arg_name in op_desc.input_arg_names():
        serial_tensor = dist_op.get_serial_input(arg_name)
        if serial_tensor.is_parameter:
            continue
        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
        if compatible_dim_mapping != dims_mapping[0]:
            dims_mapping[0] = compatible_dim_mapping
            changed = True
    for arg_name in op_desc.output_arg_names():
        serial_tensor = dist_op.get_serial_output(arg_name)
        if serial_tensor.is_parameter:
            continue
        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
        if arg_name not in xshape_arg_names:
            if compatible_dim_mapping != dims_mapping[0]:
                dims_mapping[0] = compatible_dim_mapping
                changed = True
        else:
            if compatible_dim_mapping != dims_mapping[1]:
                dims_mapping[1] = compatible_dim_mapping
                changed = True

    return changed


def update_op_dims_mapping_by_elementwise_like_dist_impl(dist_op):
    changed = False
    op_dist_attr = dist_op.dist_attr
    op_desc = dist_op.serial_op.desc
    input_arg_names = op_desc.input_arg_names()
    input_dims_mapping_dict = {}
    input_dims_mapping_lens = {}
    max_dims_mapping_len = -1
    for arg_name in input_arg_names:
        dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name)
        if max_dims_mapping_len < len(dims_mapping):
            max_dims_mapping_len = len(dims_mapping)
        input_dims_mapping_dict[arg_name] = dims_mapping
        input_dims_mapping_lens[arg_name] = len(dims_mapping)

    dims_mapping_list = []
    for arg_name in input_arg_names:
        if input_dims_mapping_lens[arg_name] < max_dims_mapping_len:
            new_dims_mapping = [-1 for _ in range(max_dims_mapping_len)]
            for i in range(input_dims_mapping_lens[arg_name]):
                new_idx = (
                    max_dims_mapping_len - input_dims_mapping_lens[arg_name]
                ) + i
                new_dims_mapping[new_idx] = input_dims_mapping_dict[arg_name][i]
            dims_mapping_list.append(new_dims_mapping)
        else:
            dims_mapping_list.append(input_dims_mapping_dict[arg_name])
    output_arg_names = op_desc.output_arg_names()
    for arg_name in output_arg_names:
        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
        assert len(dims_mapping) == max_dims_mapping_len
        dims_mapping_list.append(dims_mapping)

    compatible_dims_mapping = compute_compatible_dims_mapping(dims_mapping_list)
    assert (
        compatible_dims_mapping is not None
    ), "There is no compatible dim mapping."

    for arg_name in input_arg_names:
        if input_dims_mapping_lens[arg_name] < max_dims_mapping_len:
            new_dims_mapping = [
                -1 for _ in range(input_dims_mapping_lens[arg_name])
            ]
            for i in range(input_dims_mapping_lens[arg_name]):
                new_idx = (
                    max_dims_mapping_len - input_dims_mapping_lens[arg_name]
                ) + i
                new_dims_mapping[i] = compatible_dims_mapping[new_idx]
            if new_dims_mapping != input_dims_mapping_dict[arg_name]:
                op_dist_attr.set_input_dims_mapping(arg_name, new_dims_mapping)
                changed = True
        else:
            if compatible_dims_mapping != input_dims_mapping_dict[arg_name]:
                op_dist_attr.set_input_dims_mapping(
                    arg_name, compatible_dims_mapping
                )
                changed = True

    for arg_name in output_arg_names:
        dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name)
        if compatible_dims_mapping != dims_mapping:
            op_dist_attr.set_output_dims_mapping(
                arg_name, compatible_dims_mapping
            )
            changed = True

    return changed


def get_all_distributed_main_program(
    serial_program_info, dist_context, parallelizer
):
    "Get all distributed main programs by dist_context."
    from .dist_context import DistributedOperatorContext

    cluster = serial_program_info.cluster
    copied_parallelizer = copy.deepcopy(parallelizer)
    all_dist_main_program = []
    ranks = (
        paddle.distributed.get_world_size()
        if cluster is None
        else len(cluster.get_all_devices("GPU"))
    )
    for rank_id in range(ranks):
        used_dist_context = copy.deepcopy(dist_context)
        used_dist_context._dist_op_context = DistributedOperatorContext()
        (
            _,
            _,
            dist_startup_program,
            dist_main_program,
            _,
        ) = copied_parallelizer._get_dist_program(rank_id, used_dist_context)
        all_dist_main_program.append(dist_main_program)

    return all_dist_main_program


class SerialProgramInfo:
    def __init__(
        self, train_program, satrtup_program, loss, optimizer, cluster=None
    ):
        self._train_program = train_program
        self._startup_program = satrtup_program
        self._loss = loss
        self._optimizer = optimizer
        self._cluster = cluster

    @property
    def train_program(self):
        return self._train_program

    @property
    def startup_program(self):
        return self._startup_program

    @property
    def loss(self):
        return self._loss

    @property
    def optimizer(self):
        return self._optimizer

    @property
    def cluster(self):
        return self._cluster


def get_standalone_cost_data(distributed_programs):
    def _compute_runtime(op_cost, op, vars):
        runtime = 0
        try:
            runtime = float(op_cost["op_time"])
        except:
            return runtime
        op_config = op_cost["config"]
        total_static_input_size = 0
        total_actual_input_size = 0
        parsed_info = op_config.split("\n")
        variable = "(Variable)"
        for info in parsed_info:
            variable = (
                "(Variable)" if "(Variable)" in info else "(list<Variable>"
            )
            if variable in info:
                arg_name_lower = info[: info.find(variable) - 1]
                shape_left_boundary = info.find("[")
                shape_right_boundary = info.find("]")
                assert (
                    shape_left_boundary > 0
                    and shape_right_boundary > 0
                    and shape_right_boundary > shape_left_boundary
                ), "Get shape failed."
                shape = info[
                    shape_left_boundary + 1 : shape_right_boundary
                ].split(",")
                shape = list(map(lambda x: int(x.strip()), shape))
                dtype_factor = 1
                total_static_input_size += reduce(lambda x, y: x * y, shape)
                if op.type == "c_embedding":
                    arg_name_lower = (
                        "w" if arg_name_lower == "weight" else "ids"
                    )
                for arg_name in op.input_names:
                    if arg_name.lower() == arg_name_lower:
                        for var_name in op.input(arg_name):
                            var = vars[var_name]
                            total_actual_input_size += reduce(
                                lambda x, y: x * y, var.shape
                            )
                        break
        assert (
            total_static_input_size > 0 and total_actual_input_size > 0
        ), "Get input size failed."

        actual_runtime = (
            total_actual_input_size / total_static_input_size * runtime
        )
        return actual_runtime

    import paddle.cost_model as cm

    cost_model = cm.CostModel()
    cost_model.static_cost_data()
    DEFAULT_MULTIPLE = 2
    OP_NAME_MAPPING = {
        "c_embedding": "embedding",
        "matmul_v2": "matmul",
        "transpose2": "transpose",
        "reshape2": "reshape",
        "unsqueeze2": "unsqueeze",
        "reduce_sum": "sum",
        "elementwise_div": "divide",
    }

    standalone_cost_data = []
    # skip ops
    not_enum_ops = [
        "create_py_reader",
        "create_double_buffer_reader",
        "read",
        "assign",
    ]
    for distributed_program in distributed_programs:
        cost_data = {}
        vars = distributed_program.global_block().vars
        for op in distributed_program.global_block().ops:
            runtime = 0
            if op.type in not_enum_ops:
                cost_data[op.desc.id()] = runtime
                continue
            dtype = (
                str(vars[op.input_arg_names[0]].dtype)
                if op.input_arg_names
                else "float32"
            )
            if int(op.attr('op_role')) == int(OpRole.Backward):
                if "_grad" in op.type:
                    forward_op_name = op.type[:-5]
                    if forward_op_name in OP_NAME_MAPPING.keys():
                        forward_op_name = OP_NAME_MAPPING[forward_op_name]
                    op_cost = cost_model.get_static_op_time(
                        forward_op_name, forward=False, dtype=dtype
                    )
                    if op_cost:
                        runtime = _compute_runtime(op_cost, op, vars)
                    else:
                        op_cost = cost_model.get_static_op_time(
                            forward_op_name, dtype=dtype
                        )
                        if op_cost:
                            runtime = 2 * _compute_runtime(op_cost, op, vars)
            elif int(op.attr('op_role')) == int(OpRole.Forward):
                op_name = (
                    OP_NAME_MAPPING[op.type]
                    if op.type in OP_NAME_MAPPING.keys()
                    else op.type
                )
                op_cost = cost_model.get_static_op_time(op_name)
                if op_cost:
                    runtime = _compute_runtime(op_cost, op, vars)

            cost_data[op.desc.id()] = runtime

        standalone_cost_data.append(cost_data)

    return standalone_cost_data


def set_dist_op_desc_original_id(dist_op_desc, op_desc, dist_context):
    op_id = op_desc.id()
    op_original_id = op_desc.original_id()
    # First, try to set the original id to the id of the op_desc
    if op_id in dist_context._dist_ops_for_program:
        dist_op_desc.set_original_id(op_id)
        return
    # Second, try to set the original id to the original_id of the op_desc
    elif op_original_id in dist_context._dist_ops_for_program:
        dist_op_desc.set_original_id(op_original_id)
        return
    # Third, print error infomation if we cannot find the original id
    else:
        assert False, "Cannot find the original id in the distributed context"


def to_list(value):
    if value is None:
        return value
    if isinstance(value, (list, tuple)):
        return list(value)
    return [value]


def debug_program(program, path, name):

    filename = os.path.join(
        path, name + '_program' + ".%d" % (paddle.distributed.get_rank())
    )
    with open(filename, 'w') as f:
        f.write(str(program))


def ring_id_to_process_group(ring_id):
    for g in get_all_process_groups():
        if g.id == ring_id:
            return g
    return None


def find_higher_order_backward_op(program):

    higher_order_op_suffix = ['_grad_grad', 'triple_grad']
    for block in program.blocks:
        for op in block.ops:
            for suffix in higher_order_op_suffix:
                if suffix in op.type:
                    return True

    return False


def get_var_numel(var):
    """
    input:
        - var: variable
    return:
        number of elemnet in var
    """
    assert isinstance(var, Variable)
    assert -1 not in var.shape
    return reduce(lambda x, y: x * y, var.shape)


def get_lr(optimizer):
    if isinstance(optimizer, paddle.optimizer.Optimizer):
        return optimizer.get_lr()
    elif isinstance(optimizer, paddle.fluid.optimizer.Optimizer):
        if isinstance(optimizer._learning_rate, float):
            return optimizer._learning_rate
        else:
            return optimizer._learning_rate()
    else:
        raise TypeError(
            "'optimizer' must be object of class `paddle.optimizer.Optimizer`"
            " or `paddle.fluid.optimizer.Optimizer`, but got {}.".format(
                type(optimizer)
            )
        )


def initialize_pg_in_full_mode(all_process_groups, cur_rank):
    import socket

    from ..collective import _get_global_env

    has_recv_by_socket = []
    # This is a magic number
    magic_num = 500
    genv = _get_global_env()
    cur_rank_ip, cur_rank_port = genv.current_endpoint.split(":")
    cur_rank_recv_port = int(cur_rank_port) + magic_num
    server_socket = None
    # Large enough for recv rank
    buff_size = 1024
    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    server_socket.bind((cur_rank_ip, cur_rank_recv_port))
    # The 10 is an empirical value
    server_socket.listen(10)
    client_sockets = {}
    for process_group in all_process_groups:
        if cur_rank not in process_group.ranks:
            continue
        if len(process_group.ranks) == 2:
            index = process_group.ranks.index(cur_rank)
            is_send = True if index == 0 else False
            if is_send:
                recv_rank = process_group.ranks[1]
                recv_rank_ip, recv_rank_port = genv.trainer_endpoints[
                    recv_rank
                ].split(":")
                connect_port = int(recv_rank_port) + magic_num
                client_socket = socket.socket(
                    socket.AF_INET, socket.SOCK_STREAM
                )
                client_socket.connect((recv_rank_ip, connect_port))
                client_socket.send(str(cur_rank).encode('utf-8'))
                rank = client_socket.recv(buff_size).decode('utf-8')
                rank = int(rank)
                if rank != recv_rank:
                    raise ValueError(
                        "Please check comm pair, the recv rank should be {} but got {}.".format(
                            recv_rank, rank
                        )
                    )
                else:
                    print(
                        "It is able to instantiate {} as sender now.".format(
                            process_group.ranks
                        )
                    )
                client_socket.close()
            else:
                send_rank = process_group.ranks[0]
                while True:
                    if send_rank not in has_recv_by_socket:
                        client_socket, recv_addr = server_socket.accept()
                        rank = int(client_socket.recv(buff_size).decode())
                        client_sockets[rank] = client_socket
                        has_recv_by_socket.append(rank)
                    else:
                        client_sockets[send_rank].send(
                            str(cur_rank).encode("utf-8")
                        )
                        client_sockets[send_rank].close()
                        print(
                            "It is able to instantiate {} as recver now.".format(
                                process_group.ranks
                            )
                        )
                        break
        process_group.instantiate()
    server_socket.close()


def is_recompute_op(op):
    return op.has_attr('op_namescope') and "/auto_parallel/rc" in op.attr(
        'op_namescope'
    )


def set_recompute_segments(model, losses, strategy, program):
    from ..passes.auto_parallel_recompute import RecomputeState

    if not losses:
        return

    recompute = strategy.recompute
    if not recompute.enable:
        return

    # NOTE: hack to enable recompute in engine api for GPT-3
    # TODO support more PaddleNLP/CV models here
    # extract ckpts by specific model
    ckpts = []
    if isinstance(model, paddle.nn.Layer):
        if (
            hasattr(model, "gpt")
            and model.__class__.__name__
            in [
                'GPTForPretraining',
                'GPTForPretrainingAuto',
            ]
            and hasattr(model.gpt, "checkpoints")
        ):
            ckpts = model.gpt.checkpoints
            # last recompute segment is not need to recompute
            if len(ckpts) > 2:
                ckpts.pop()
        else:
            ckpts = recompute.checkpoints
    else:
        ckpts = recompute.checkpoints

    if not ckpts:
        return

    block = program.global_block()
    rc_state = RecomputeState(block, block.ops)
    rc_state.build_stats()
    checkpoints = rc_state.sort_checkpoints(ckpts)

    segments = []
    start_idx = -1
    pre_segment_end_idx = -1
    while start_idx + 1 < len(checkpoints):
        if start_idx == -1:
            ckpt_name = checkpoints[start_idx + 1]
            if ckpt_name not in rc_state.var_op_deps:
                start_idx += 1
                continue
            op_idx_list = rc_state.var_op_deps[ckpt_name]["var_as_output_ops"]
            if op_idx_list and max(op_idx_list) > 0:
                segments.append([0, max(op_idx_list) + 1])
        else:
            flag, min_idx, max_idx = rc_state.is_subgraph(
                [checkpoints[start_idx]], [checkpoints[start_idx + 1]]
            )
            if flag:
                min_idx = rc_state._update_segment_start(
                    min_idx, pre_segment_end_idx
                )
                segments.append([min_idx, max_idx + 1])
            else:
                logging.debug(
                    "Could not recompute op range [{}] - [{}] ".format(
                        min_idx, max_idx + 1
                    )
                )
        start_idx += 1

    for i, segment in enumerate(segments):
        for j in range(segment[0], segment[1]):
            block.ops[j]._set_attr(
                'op_namescope', "/auto_parallel/rc_" + str(i)
            )


def get_input_split_info(cur_rank, var, dist_context):
    # deduce how the input data is split among the cluster
    tensor_dist_attr = dist_context.get_tensor_dist_attr_for_program(var)
    process_mesh = tensor_dist_attr.process_mesh
    dims_mapping = tensor_dist_attr.dims_mapping

    if cur_rank not in process_mesh.process_ids:
        rank_id = _get_corresponding_rank(dist_context, process_mesh, cur_rank)
    else:
        rank_id = cur_rank

    batch_size_axis = dims_mapping[0]
    if batch_size_axis > -1 and process_mesh.shape[batch_size_axis] > 1:
        group_ranks = _get_comm_group(
            process_mesh.process_ids,
            process_mesh.shape,
            batch_size_axis,
            rank_id,
        )
        return len(group_ranks), group_ranks.index(rank_id)

    return 1, 0


def validate_opt(optimizer):
    if optimizer is not None:
        optimizer._parameter_list = None
        optimizer._param_groups = None
    return optimizer


def set_data_parallel(x):
    from .interface import ProcessMesh, shard_tensor
    from .process_group import get_world_process_group

    world_ranks = get_world_process_group().ranks
    process_mesh = ProcessMesh(world_ranks, ['dp'])
    shard_spec = ['dp' if len(world_ranks) > 1 else None] + [
        None for _ in range(len(x.shape) - 1)
    ]

    return shard_tensor(x, process_mesh, shard_spec)


def is_naive_data_parallel(dist_context):
    # Navie data parallel only completes dist_attr once from the front to back.
    if not dist_context.data_parallel:
        return False

    ops_type = [
        op.type
        for op in dist_context._original_serial_main_program.global_block().ops
    ]
    if (
        not set(ops_type) & set(__not_naive_data_parallel_op__)
    ) and dist_context.data_parallel:
        return True
    return False


def _copy_tensor_dist_attr_to_cpp(cpp_dist_attr, py_dist_attr):
    py_process_mesh = py_dist_attr.process_mesh
    if py_process_mesh is not None:
        cpp_dist_attr.process_mesh = core.ProcessMesh(
            py_process_mesh.shape,
            py_process_mesh.process_ids,
            ["d" + str(i) for i in range(len(py_process_mesh.shape))],
        )
    cpp_dist_attr.dims_mapping = py_dist_attr.dims_mapping
    cpp_dist_attr.annotated = py_dist_attr.annotated


def _copy_tensor_dist_attr_from_cpp(cpp_dist_attr, py_dist_attr):
    from .process_mesh import ProcessMesh

    cpp_process_mesh = cpp_dist_attr.process_mesh
    if cpp_process_mesh is not None:
        py_dist_attr.process_mesh = ProcessMesh(
            shape=cpp_process_mesh.shape,
            process_ids=cpp_process_mesh.process_ids,
        )
    py_dist_attr.dims_mapping = cpp_dist_attr.dims_mapping
    py_dist_attr.annotated = cpp_dist_attr.annotated


def _copy_op_dist_attr_to_cpp(cpp_dist_attr, py_dist_attr):
    py_process_mesh = py_dist_attr.process_mesh
    if py_process_mesh is not None:
        cpp_dist_attr.process_mesh = core.ProcessMesh(
            py_process_mesh.shape,
            py_process_mesh.process_ids,
            ["d" + str(i) for i in range(len(py_process_mesh.shape))],
        )
    cpp_dist_attr.impl_type = py_dist_attr.impl_type
    cpp_dist_attr.impl_idx = py_dist_attr.impl_idx
    cpp_dist_attr.is_recompute = py_dist_attr.is_recompute
    cpp_dist_attr.annotated = py_dist_attr.annotated
    for name, py_tensor_dist_attr in py_dist_attr.inputs_dist_attrs.items():
        cpp_tensor_dist_attr = cpp_dist_attr.get_input_dist_attr(name)
        _copy_tensor_dist_attr_to_cpp(cpp_tensor_dist_attr, py_tensor_dist_attr)
    for name, py_tensor_dist_attr in py_dist_attr.outputs_dist_attrs.items():
        cpp_tensor_dist_attr = cpp_dist_attr.get_output_dist_attr(name)
        _copy_tensor_dist_attr_to_cpp(cpp_tensor_dist_attr, py_tensor_dist_attr)


def _copy_op_dist_attr_from_cpp(cpp_dist_attr, py_dist_attr):
    from .process_mesh import ProcessMesh

    cpp_process_mesh = cpp_dist_attr.process_mesh
    if cpp_process_mesh is not None:
        py_dist_attr.process_mesh = ProcessMesh(
            shape=cpp_process_mesh.shape,
            process_ids=cpp_process_mesh.process_ids,
        )
    py_dist_attr.impl_type = cpp_dist_attr.impl_type
    py_dist_attr.impl_idx = cpp_dist_attr.impl_idx
    py_dist_attr.is_recompute = cpp_dist_attr.is_recompute
    py_dist_attr.annotated = cpp_dist_attr.annotated
    for name, cpp_tensor_dist_attr in cpp_dist_attr.inputs_dist_attrs.items():
        py_tensor_dist_attr = py_dist_attr.get_input_dist_attr(name)
        _copy_tensor_dist_attr_from_cpp(
            cpp_tensor_dist_attr, py_tensor_dist_attr
        )
    for name, cpp_tensor_dist_attr in cpp_dist_attr.outputs_dist_attrs.items():
        py_tensor_dist_attr = py_dist_attr.get_output_dist_attr(name)
        _copy_tensor_dist_attr_from_cpp(
            cpp_tensor_dist_attr, py_tensor_dist_attr
        )


def _copy_dist_attr_to_cpp(dist_context):
    for dist_tensor in dist_context._dist_tensors_for_program.values():
        _copy_tensor_dist_attr_to_cpp(
            dist_tensor.serial_tensor.dist_attr, dist_tensor.dist_attr
        )

    for dist_op in dist_context._dist_ops_for_program.values():
        _copy_op_dist_attr_to_cpp(
            dist_op.serial_op.dist_attr, dist_op.dist_attr
        )


def _copy_dist_attr_from_cpp(dist_context):
    for dist_tensor in dist_context._dist_tensors_for_program.values():
        _copy_tensor_dist_attr_from_cpp(
            dist_tensor.serial_tensor.dist_attr, dist_tensor.dist_attr
        )

    for dist_op in dist_context._dist_ops_for_program.values():
        _copy_op_dist_attr_from_cpp(
            dist_op.serial_op.dist_attr, dist_op.dist_attr
        )


def _copy_dist_attr_to_cpp_for_graph(dist_context):
    for node in dist_context.serial_ordered_nodes:
        if node.is_var() and node.var() is not None:
            py_dist_attr = dist_context.get_tensor_dist_attr_for_graph(node)
            cpp_dist_attr = node.var().dist_attr
            _copy_tensor_dist_attr_to_cpp(cpp_dist_attr, py_dist_attr)
        if node.is_op() and node.op() is not None:
            py_dist_attr = dist_context.get_op_dist_attr_for_graph(node)
            cpp_dist_attr = node.op().dist_attr
            _copy_op_dist_attr_to_cpp(cpp_dist_attr, py_dist_attr)


def _copy_dist_attr_from_cpp_for_graph(dist_context):
    for node in dist_context.serial_ordered_nodes:
        if node.is_var() and node.var() is not None:
            py_dist_attr = dist_context.get_tensor_dist_attr_for_graph(node)
            cpp_dist_attr = node.var().dist_attr
            _copy_tensor_dist_attr_from_cpp(cpp_dist_attr, py_dist_attr)
        if node.is_op() and node.op() is not None:
            py_dist_attr = dist_context.get_op_dist_attr_for_graph(node)
            cpp_dist_attr = node.op().dist_attr
            _copy_op_dist_attr_from_cpp(cpp_dist_attr, py_dist_attr)


def insert_dependencies_for_two_ops(
    block,
    idx,
    prior_op,
    posterior_op,
    dist_context,
    is_recompute=False,
    sync=False,
    op_namescope=None,
):
    """
    dependency: prior_op should be run before posterior_op
    """

    assert (
        len(prior_op.output_arg_names) >= 1
    ), "first op of dependency should at least have one output. [{}]".format(
        str(prior_op)
    )
    assert (
        len(posterior_op.input_arg_names) >= 1
    ), "second op of dependency should at least have one input. [{}]".format(
        str(posterior_op)
    )
    prior_op_mesh = dist_context.get_op_dist_attr_for_program(
        prior_op
    ).process_mesh
    posterior_mesh = dist_context.get_op_dist_attr_for_program(
        posterior_op
    ).process_mesh
    assert (
        prior_op_mesh == posterior_mesh
    ), "two ops of dependency should have same mesh but got [{}] and [{}]".format(
        str(prior_op_mesh), str(posterior_mesh)
    )

    def _select_best_depend_var(vars):

        # parameter should not be dep var since it maybe partition in sharding pass
        vars = [var for var in vars if not var.is_parameter]
        assert len(vars) > 0
        vars_with_numels = [(var, get_var_numel(var)) for var in vars]
        vars_with_numels.sort(key=lambda x: x[1])

        return vars_with_numels[-1][0]

    first_var = _select_best_depend_var(
        [block.var(name) for name in prior_op.output_arg_names]
    )
    second_var = _select_best_depend_var(
        [block.var(name) for name in posterior_op.input_arg_names]
    )

    return insert_dependencies_for_vars(
        block,
        idx,
        first_var,
        second_var,
        dist_context,
        OpRole.Backward,
        process_mesh=prior_op_mesh,
        is_recompute=is_recompute,
        sync=sync,
        op_namescope=op_namescope,
        use_nop=False,
    )


def insert_dependencies_for_vars(
    block,
    idx,
    prior_vars,
    post_vars,
    dist_context,
    oprole,
    process_mesh=None,
    is_recompute=False,
    sync=False,
    op_namescope=None,
    use_nop=False,
):
    """
    dependency: op that generates prior_vars should be run before op that generates post_vars
    """

    if isinstance(prior_vars, Variable):
        prior_vars = [prior_vars]
    if isinstance(post_vars, Variable):
        post_vars = [post_vars]
    for prior_var in prior_vars:
        assert block.has_var(prior_var.name)
    for post_var in post_vars:
        assert block.has_var(post_var.name)

    if process_mesh is None:
        process_mesh = dist_context.get_tensor_dist_attr_for_program(
            post_vars[0]
        ).process_mesh
    assert process_mesh is not None

    use_nop = True
    if use_nop:
        depend_op = block._insert_op_without_sync(
            idx,
            type='nop',
            inputs={
                "X": prior_vars,
            },
            outputs={"Out": post_vars},
        )
    else:
        depend_op = block._insert_op_without_sync(
            idx,
            type='depend',
            inputs={
                "X": post_vars,
                "Dep": prior_vars,
            },
            outputs={"Out": post_vars},
        )

    # depend_op.desc.set_type("depend")
    depend_op._set_attr(OP_ROLE_KEY, oprole)
    # depend_op.desc.set_input("Dep", [first_var.name])
    # self.desc.set_output(out_proto.name, out_arg_names)

    naive_set_dist_op_attr_for_program_by_mesh(
        depend_op, process_mesh, dist_context, is_recompute
    )
    if op_namescope is not None:
        depend_op._set_attr('op_namescope', "/{}".format(op_namescope))

    if sync:
        block._sync_with_cpp()

    return depend_op


def is_dep_skip_op(op):
    if "c_" in op.type:
        return True

    return False


def use_standalone_executor():
    return os.environ.get('FLAGS_CONVERT_GRAPH_TO_PROGRAM', None) in [
        1,
        '1',
        True,
        'True',
        'true',
    ]