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 os
import copy
import paddle
import threading
import numpy as np
import warnings
import logging
from functools import reduce

import paddle.fluid.core as core
from paddle.distributed.fleet.meta_optimizers.common import OpRole
from paddle.fluid.io import is_parameter, is_belong_to_optimizer
from paddle.distributed.auto_parallel.dist_attribute import TensorDistributedAttribute, OperatorDistributedAttribute


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 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
    from .dist_context import 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.processes and mesh.topology == target_mesh.topology:
            coordinate = _linear_idx2coordinate(mesh.topology,
                                                mesh.processes.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.processes[_coordinate2linear_idx(mesh.topology,
                                                            coordinate)]
    else:
        return target_mesh.processes[0]


def _get_unshard_dist_shape(var, dist_attr):
    var_shape = var.shape
    mapping = dist_attr.dims_mapping
    mesh = dist_attr.process_mesh.topology
    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.topology,
                "process_group": process_mesh.processes,
                "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 .operators.common import infer_shape
    from paddle.distributed.fleet.meta_optimizers.common import OpRole

    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_zeros_like"
            ]:
                forward_var_name = op.input_arg_names[0]
            elif op.type == "matmul_v2_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"
            ]
            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"
            ]
            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)


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


def is_forward_op(op):
    ref_role1 = int(core.op_proto_and_checker_maker.OpRole.Forward)
    ref_role2 = int(core.op_proto_and_checker_maker.OpRole.Loss)
    op_role = int(op.attr('op_role'))
    return OP_ROLE_KEY in op.attr_names and (op_role == ref_role1 or
                                             op_role == ref_role2)


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_loss_op(op):
    return OP_ROLE_KEY in op.attr_names and \
        int(op.all_attrs()[OP_ROLE_KEY]) == (int(core.op_proto_and_checker_maker.OpRole.Forward) | int(core.op_proto_and_checker_maker.OpRole.Loss))


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 = TensorDistributedAttribute()
    tensor_dist_attr.dims_mapping = dims_mapping
    # TODO get global mesh group
    tensor_dist_attr.process_mesh = 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 = OperatorDistributedAttribute()

    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 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, DistributedContext
    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 = []
    not_enum_ops = ["create_py_reader", "create_double_buffer_reader", "read"]
    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]