io.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import os
import errno
import time
import shutil
import six

from paddle.fluid.executor import Executor
from paddle.fluid.evaluator import Evaluator
from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable
from . import core

__all__ = [
    'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
    'load_persistables', 'save_inference_model', 'load_inference_model'
]


def is_parameter(var):
    """
    Check whether the given variable is an instance of Parameter.

    Args:
        var(Variable): The variable to be checked.

    Returns:
        bool: True if the given `var` is an instance of Parameter,
        False if not.

    Examples:
        .. code-block:: python

            param = fluid.default_main_program().global_block().var('fc.w')
            res = fluid.io.is_parameter(param)
    """
    return isinstance(var, Parameter)


def is_persistable(var):
    """
    Check whether the given variable is persistable.

    Args:
        var(Variable): The variable to be checked.

    Returns:
        bool: True if the given `var` is persistable
        False if not.

    Examples:
        .. code-block:: python

            param = fluid.default_main_program().global_block().var('fc.b')
            res = fluid.io.is_persistable(param)
    """
    if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
            var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
            var.desc.type() == core.VarDesc.VarType.READER:
        return False
    return var.persistable


def _clone_var_in_block_(block, var):
    assert isinstance(var, Variable)
    return block.create_var(
        name=var.name,
        shape=var.shape,
        dtype=var.dtype,
        type=var.type,
        lod_level=var.lod_level,
        persistable=True)


def save_vars(executor,
              dirname,
              main_program=None,
              vars=None,
              predicate=None,
              filename=None):
    """
    Save variables to the given directory by executor.

    There are two ways to specify variables to be saved: The first way, list
    variables in a list and assign it to the `vars`. The second way, assign the
    `main_program` with an existing program, then all variables in the program
    will be saved. The first way has a higher priority. In other words, if `vars`
    are assigned, the `main_program` and the `predicate` will be ignored.

    The `dirname` are used to specify the folder where to save variables.
    If you prefer to save variables in separate files in the folder `dirname`,
    set `filename` None; if you prefer to save all variables in a single file,
    use `filename` to specify it.

    Args:
        executor(Executor): The executor to run for saving variables.
        dirname(str): The directory path.
        main_program(Program|None): The program whose variables will be saved.
                                    If it is None, the default main program will
                                    be used automatically.
                                    Default: None
        vars(list[Variable]|None): The list that contains all variables to save.
                                   It has a higher priority than the `main_program`.
                                   Default: None
        predicate(function|None): If it is not None, only variables in the
                                  `main_program` that makes predicate(variable)==True
                                  will be saved. It only works when we are using the
                                  `main_program` to specify variables (In other words
                                  `vars` is None).
                                  Default: None
        filename(str|None): The file which to save all variables. If you prefer to save
                            variables separately, set it to None.
                            Default: None

    Returns:
        None

    Raises:
        TypeError: If `main_program` is not an instance of Program nor None.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"

            # The first usage: using `main_program` to specify variables
            def name_has_fc(var):
                res = "fc" in var.name
                return res

            prog = fluid.default_main_program()
            fluid.io.save_vars(executor=exe, dirname=path, main_program=prog,
                               vars=None)
            # All variables in `main_program` whose name includes "fc" will be saved.
            # And variables are going to be saved separately.


            # The second usage: using `vars` to specify variables
            var_list = [var_a, var_b, var_c]
            fluid.io.save_vars(executor=exe, dirname=path, vars=var_list,
                               filename="vars_file")
            # var_a, var_b and var_c will be saved. And they are going to be
            # saved in the same file named 'var_file' in the path "./my_paddle_model".
    """
    if vars is None:
        if main_program is None:
            main_program = default_main_program()
        if not isinstance(main_program, Program):
            raise TypeError("program should be as Program type or None")

        save_vars(
            executor,
            main_program=main_program,
            dirname=dirname,
            vars=list(filter(predicate, main_program.list_vars())),
            filename=filename)
    else:
        save_program = Program()
        save_block = save_program.global_block()

        if main_program is None:
            main_program = default_main_program()
        if not isinstance(main_program, Program):
            raise TypeError("program should be as Program type or None")

        save_var_map = {}
        for each_var in vars:
            # NOTE: don't save the variable which type is RAW
            if each_var.type == core.VarDesc.VarType.RAW:
                continue
            if each_var.name == main_program._distributed_lookup_table:
                continue
            new_var = _clone_var_in_block_(save_block, each_var)
            if filename is None:
                save_block.append_op(
                    type='save',
                    inputs={'X': [new_var]},
                    outputs={},
                    attrs={'file_path': os.path.join(dirname, new_var.name)})
            else:
                save_var_map[new_var.name] = new_var

        if filename is not None:
            save_var_list = []
            for name in sorted(save_var_map.keys()):
                save_var_list.append(save_var_map[name])

            save_block.append_op(
                type='save_combine',
                inputs={'X': save_var_list},
                outputs={},
                attrs={'file_path': os.path.join(dirname, filename)})

        # if there is lookup table, the trainer 0 will notify all pserver to save.
        if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
            lookup_table_filename = os.path.join(dirname, "__lookup_table__")
            attrs = {}
            attrs['epmap'] = main_program._endpoints
            attrs['dir'] = lookup_table_filename
            attrs['lookup_table'] = main_program._distributed_lookup_table
            save_block.append_op(
                type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)

        executor.run(save_program)


def save_params(executor, dirname, main_program=None, filename=None):
    """
    This function filters out all parameters from the give `main_program`
    and then save them to the folder `dirname` or the file `filename`.

    Use the `dirname` to specify the saving folder. If you would like to
    save parameters in separate files, set `filename` None; if you would
    like to save all parameters in a single file, use `filename` to specify
    the file name.

    NOTICE: Some variables are not Parameter while they are necessary for
    training. So you can NOT save and continue your training just by
    `save_params()` and `load_params()`. Please use `save_persistables()`
    and `load_persistables()` instead.

    Args:
        executor(Executor): The executor to run for saving parameters.
        dirname(str): The saving directory path.
        main_program(Program|None): The program whose parameters will be
                                    saved. If it is None, the default
                                    main program will be used automatically.
                                    Default: None
        filename(str|None): The file to save all parameters. If you prefer
                            to save parameters in differnet files, set it
                            to None.
                            Default: None

    Returns:
        None

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"
            prog = fluid.default_main_program()
            fluid.io.save_params(executor=exe, dirname=param_path,
                                 main_program=None)
    """
    save_vars(
        executor,
        dirname=dirname,
        main_program=main_program,
        vars=None,
        predicate=is_parameter,
        filename=filename)


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

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

    Args:
        executor(Executor): The executor to run for saving persistable variables.
        dirname(str): The directory path.
        main_program(Program|None): The program whose persistbale variables will
                                    be saved. If it is None, the default main
                                    program will be used automatically.
                                    Default: None
        filename(str|None): The file to saved all variables. If you prefer to
                            save variables in differnet files, set it to None.
                            Default: None

    Returns:
        None

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"
            prog = fluid.default_main_program()
            fluid.io.save_persistables(executor=exe, dirname=param_path,
                                       main_program=None)
    """
    save_vars(
        executor,
        dirname=dirname,
        main_program=main_program,
        vars=None,
        predicate=is_persistable,
        filename=filename)


def load_vars(executor,
              dirname,
              main_program=None,
              vars=None,
              predicate=None,
              filename=None):
    """
    Load variables from the given directory by executor.

    There are two ways to specify variables to be loaded: The first way, list
    variables in a list and assign it to the `vars`. The second way, assign the
    `main_program` with an existing program, then all variables in the program
    will be loaded. The first way has a higher priority. In other words if `vars`
    are assigned, the `main_program` and the `predicate` will be ignored.

    The `dirname` are used to specify the folder where to load variables.
    If variables were saved in separate files in the folder `dirname`,
    set `filename` None; if all variables were saved in a single file,
    use `filename` to specify it.

    Args:
        executor(Executor): The executor to run for loading variables.
        dirname(str): The directory path.
        main_program(Program|None): The program whose variables will be loaded.
                                    If it is None, the default main program will
                                    be used automatically.
                                    Default: None
        vars(list[Variable]|None): The list that contains all variables to load.
                                   It has a higher priority than the `main_program`.
                                   Default: None
        predicate(function|None): If it is not None, only variables in the
                                  `main_program` that makes predicate(variable)==True
                                  will be loaded. It only works when we are using the
                                  `main_program` to specify variables (In other words
                                  `vars` is None).
                                  Default: None
        filename(str|None): The file which saved all required variables. If variables
                            were saved in differnet files, set it to None.
                            Default: None

    Returns:
        None

    Raises:
        TypeError: If `main_program` is not an instance of Program nor None.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"

            # The first usage: using `main_program` to specify variables
            def name_has_fc(var):
                res = "fc" in var.name
                return res

            prog = fluid.default_main_program()
            fluid.io.load_vars(executor=exe, dirname=path, main_program=prog,
                               vars=None)
            # All variables in `main_program` whose name includes "fc" will be loaded.
            # And all the variables are supposed to have been saved in differnet files.


            # The second usage: using `vars` to specify variables
            var_list = [var_a, var_b, var_c]
            fluid.io.load_vars(executor=exe, dirname=path, vars=var_list,
                               filename="vars_file")
            # var_a, var_b and var_c will be loaded. And they are supposed to haven
            # been saved in the same file named 'var_file' in the path "./my_paddle_model".
    """
    if vars is None:
        if main_program is None:
            main_program = default_main_program()
        if not isinstance(main_program, Program):
            raise TypeError("program's type should be Program")

        load_vars(
            executor,
            dirname=dirname,
            main_program=main_program,
            vars=list(filter(predicate, main_program.list_vars())),
            filename=filename)
    else:
        load_prog = Program()
        load_block = load_prog.global_block()

        if main_program is None:
            main_program = default_main_program()
        if not isinstance(main_program, Program):
            raise TypeError("program should be as Program type or None")

        load_slice_vars = []
        for each_var in main_program._slice_vars_and_attrs:
            load_slice_vars.append(each_var[2].name)

        load_var_map = {}
        for each_var in vars:
            assert isinstance(each_var, Variable)
            if each_var.type == core.VarDesc.VarType.RAW:
                continue
            if each_var.name in load_slice_vars:
                continue
            new_var = _clone_var_in_block_(load_block, each_var)
            if filename is None:
                load_block.append_op(
                    type='load',
                    inputs={},
                    outputs={'Out': [new_var]},
                    attrs={'file_path': os.path.join(dirname, new_var.name)})
            else:
                load_var_map[new_var.name] = new_var

        if filename is not None:
            load_var_list = []
            for name in sorted(load_var_map.keys()):
                load_var_list.append(load_var_map[name])

            load_block.append_op(
                type='load_combine',
                inputs={},
                outputs={"Out": load_var_list},
                attrs={'file_path': os.path.join(dirname, filename)})
        executor.run(load_prog)

        # load slice vars on pserver, if have it.
        _load_slice_up_vars(executor, dirname,
                            main_program._slice_vars_and_attrs)


def load_params(executor, dirname, main_program=None, filename=None):
    """
    This function filters out all parameters from the give `main_program`
    and then trys to load these parameters from the folder `dirname` or
    the file `filename`.

    Use the `dirname` to specify the folder where parameters were saved. If
    parameters were saved in separate files in the folder `dirname`, set
    `filename` None; if all parameters were saved in a single file, use
    `filename` to specify the file name.

    NOTICE: Some variables are not Parameter while they are necessary for
    training. So you can NOT save and continue your training just by
    `save_params()` and `load_params()`. Please use `save_persistables()`
    and `load_persistables()` instead.

    Args:
        executor(Executor): The executor to run for loading parameters.
        dirname(str): The directory path.
        main_program(Program|None): The program whose parameters will be
                                    loaded. If it is None, the default
                                    main program will be used automatically.
                                    Default: None
        filename(str|None): The file which saved all parameters. If parameters
                            were saved in differnet files, set it to None.
                            Default: None

    Returns:
        None

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"
            prog = fluid.default_main_program()
            fluid.io.load_params(executor=exe, dirname=param_path,
                                main_program=None)
    """
    load_vars(
        executor,
        dirname=dirname,
        main_program=main_program,
        predicate=is_parameter,
        filename=filename)


def load_persistables(executor, dirname, main_program=None, filename=None):
    """
    This function filters out all variables with `persistable==True` from the
    give `main_program` and then trys to load these variables from the folder
    `dirname` or the file `filename`.

    Use the `dirname` to specify the folder where persistable variables were
    saved. If variables were saved in separate files, set `filename` None;
    if all variables were saved in a single file, use `filename` to specify
    the file name.

    Args:
        executor(Executor): The executor to run for loading persistable variables.
        dirname(str): The directory path.
        main_program(Program|None): The program whose persistbale variables will
                                    be loaded. If it is None, the default main
                                    program will be used automatically.
                                    Default: None
        filename(str|None): The file which saved all variables. If variables were
                            saved in differnet files, set it to None.
                            Default: None

    Returns:
        None

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"
            prog = fluid.default_main_program()
            fluid.io.load_persistables(executor=exe, dirname=param_path,
                                       main_program=None)
    """
    load_vars(
        executor,
        dirname=dirname,
        main_program=main_program,
        predicate=is_persistable,
        filename=filename)


def prepend_feed_ops(inference_program,
                     feed_target_names,
                     feed_holder_name='feed'):
    if len(feed_target_names) == 0:
        return

    global_block = inference_program.global_block()
    feed_var = global_block.create_var(
        name=feed_holder_name,
        type=core.VarDesc.VarType.FEED_MINIBATCH,
        persistable=True)

    for i, name in enumerate(feed_target_names):
        out = global_block.var(name)
        global_block._prepend_op(
            type='feed',
            inputs={'X': [feed_var]},
            outputs={'Out': [out]},
            attrs={'col': i})


def append_fetch_ops(inference_program,
                     fetch_target_names,
                     fetch_holder_name='fetch'):
    global_block = inference_program.global_block()
    fetch_var = global_block.create_var(
        name=fetch_holder_name,
        type=core.VarDesc.VarType.FETCH_LIST,
        persistable=True)

    for i, name in enumerate(fetch_target_names):
        global_block.append_op(
            type='fetch',
            inputs={'X': [name]},
            outputs={'Out': [fetch_var]},
            attrs={'col': i})


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

    Args:
        dirname(str): The directory path to save the inference model.
        feeded_var_names(list[str]): Names of variables that need to be feeded data
                                     during inference.
        target_vars(list[Variable]): Variables from which we can get inference
                                     results.
        executor(Executor): The executor that saves the inference model.
        main_program(Program|None): The original program, which will be pruned to
                                    build the inference model. If is setted None,
                                    the default main program will be used.
                                    Default: None.
        model_filename(str|None): The name of file to save the inference program
                                  itself. If is setted None, a default filename
                                  `__model__` will be used.
        params_filename(str|None): The name of file to save all related parameters.
                                   If it is setted None, parameters will be saved
                                   in separate files .
        export_for_deployment(bool): If True, programs are modified to only support
                                     direct inference deployment. Otherwise,
                                     more information will be stored for flexible
                                     optimization and re-training. Currently, only
                                     True is supported.

    Returns:
        None

    Raises:
        ValueError: If `feed_var_names` is not a list of basestring.
        ValueError: If `target_vars` is not a list of Variable.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            path = "./infer_model"
            fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'],
                         target_vars=[predict_var], executor=exe)

            # In this exsample, the function will prune the default main program
            # to make it suitable for infering the `predict_var`. The pruned
            # inference program is going to be saved in the "./infer_model/__model__"
            # and parameters are going to be saved in separate files under folder
            # "./infer_model".

    """
    if isinstance(feeded_var_names, six.string_types):
        feeded_var_names = [feeded_var_names]
    elif export_for_deployment:
        if len(feeded_var_names) > 0:
            # TODO(paddle-dev): polish these code blocks
            if not (bool(feeded_var_names) and all(
                    isinstance(name, six.string_types)
                    for name in feeded_var_names)):
                raise ValueError("'feed_var_names' should be a list of str.")

    if isinstance(target_vars, Variable):
        target_vars = [target_vars]
    elif export_for_deployment:
        if not (bool(target_vars) and all(
                isinstance(var, Variable) for var in target_vars)):
            raise ValueError("'target_vars' should be a list of Variable.")

    if main_program is None:
        main_program = default_main_program()

    # when a pserver and a trainer running on the same machine, mkdir may conflict
    try:
        os.makedirs(dirname)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

    if model_filename is not None:
        model_basename = os.path.basename(model_filename)
    else:
        model_basename = "__model__"
    model_basename = os.path.join(dirname, model_basename)

    # When export_for_deployment is true, we modify the program online so that
    # it can only be loaded for inference directly. If it's false, the whole
    # original program and related meta are saved so that future usage can be
    # more flexible.

    origin_program = main_program.clone()

    if export_for_deployment:
        main_program = main_program.clone()
        global_block = main_program.global_block()
        for i, op in enumerate(global_block.ops):
            op.desc.set_is_target(False)
            if op.type == "feed" or op.type == "fetch":
                global_block._remove_op(i)
        main_program.desc.flush()

        main_program = main_program._prune(targets=target_vars)
        main_program = main_program._inference_optimize(prune_read_op=True)
        fetch_var_names = [v.name for v in target_vars]

        prepend_feed_ops(main_program, feeded_var_names)
        append_fetch_ops(main_program, fetch_var_names)

        with open(model_basename, "wb") as f:
            f.write(main_program.desc.serialize_to_string())
    else:
        # TODO(panyx0718): Save more information so that it can also be used
        # for training and more flexible post-processing.
        with open(model_basename + ".main_program", "wb") as f:
            f.write(main_program.desc.serialize_to_string())

    main_program._copy_dist_param_info_from(origin_program)

    if params_filename is not None:
        params_filename = os.path.basename(params_filename)

    save_persistables(executor, dirname, main_program, params_filename)


def load_inference_model(dirname,
                         executor,
                         model_filename=None,
                         params_filename=None,
                         pserver_endpoints=None):
    """
    Load inference model from a directory

    Args:
        dirname(str): The directory path
        executor(Executor): The executor to run for loading inference model.
        model_filename(str|None): The name of file to load inference program.
                                  If it is None, the default filename
                                  '__model__' will be used.
                                  Default: None
        params_filename(str|None): The name of file to load all parameters.
                                   It is only used for the case that all
                                   parameters were saved in a single binary
                                   file. If parameters were saved in separate
                                   files, set it as 'None'.
        pserver_endpoints(list|None): This only need by distributed inference.
                                    When use distributed look up table in training,
                                    We also need it in inference.The parameter is
                                    a list of pserver endpoints.

    Returns:
        tuple: The return of this function is a tuple with three elements:
        (program, feed_target_names, fetch_targets). The `program` is a
        Program, it's the program for inference. The `feed_target_names` is
        a list of str, it contains Names of variables that need to feed
        data in the inference program. The `fetch_targets` is a list of
        Variable. It contains variables from which we can get inference
        results.

    Raises:
        ValueError: If `dirname` is not a existing directory.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            path = "./infer_model"
            endpoints = ["127.0.0.1:2023","127.0.0.1:2024"]
            [inference_program, feed_target_names, fetch_targets] =
                fluid.io.load_inference_model(dirname=path, executor=exe)
            results = exe.run(inference_program,
                          feed={feed_target_names[0]: tensor_img},
                          fetch_list=fetch_targets)

            # if we need lookup table, we will use:
            fluid.io.load_inference_model(dirname=path, executor=exe, pserver_endpoints=endpoints)

            # In this exsample, the inference program was saved in the
            # "./infer_model/__model__" and parameters were saved in
            # separate files in ""./infer_model".
            # After getting inference program, feed target names and
            # fetch targets, we can use an Executor to run the inference
            # program to get the inference result.

    """
    if not os.path.isdir(dirname):
        raise ValueError("There is no directory named '%s'", dirname)

    if model_filename is not None:
        model_filename = os.path.basename(model_filename)
    else:
        model_filename = "__model__"
    model_filename = os.path.join(dirname, model_filename)

    if params_filename is not None:
        params_filename = os.path.basename(params_filename)

    with open(model_filename, "rb") as f:
        program_desc_str = f.read()

    program = Program.parse_from_string(program_desc_str)
    if not core._is_program_version_supported(program._version()):
        raise ValueError("Unsupported program version: %d\n" %
                         program._version())
    # Binary data also need versioning.
    load_persistables(executor, dirname, program, params_filename)

    if pserver_endpoints:
        program = _endpoints_replacement(program, pserver_endpoints)

    feed_target_names = program.desc.get_feed_target_names()
    fetch_target_names = program.desc.get_fetch_target_names()
    fetch_targets = [
        program.global_block().var(name) for name in fetch_target_names
    ]

    return [program, feed_target_names, fetch_targets]


def _save_lookup_tables_by_notify(executor, dirname, lookup_table,
                                  pserver_endpoints):
    """
    This function will send checkpoint notify message from Trainer 0
    to all the pservers.
    The checkpoint notify message contains lookup table name,
    the absolute path on pserver to save lookup_table.

    Args:
        executor(Executor): The executor to run for send checkpoint notify.
        dirname(str): The folder where to save.
        lookup_table(string): the lookup table name, when use distribute
            lookup table, we can get lookup table name by DistributeTranspiler.
            table_name
        ps_endpoint_list(list): the parameter server ip:port list.
            when use distribute lookup table, we can get ps_endpoint_list by
            distribute arguments.
    Return:
        None

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param_path = "./my_paddle_model"
            table_name = "share_w"
            ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]

            _save_pserver_vars_by_notify(executor=exe,
                    dirname=param_path, lookup_table=table_name,
                    pserver_endpoints=ps_endpoints)
    """

    pserver_notify_program = Program()
    pserver_notify_block = pserver_notify_program.global_block()

    attrs = {}
    attrs['epmap'] = pserver_endpoints
    attrs['dir'] = dirname
    attrs['lookup_table'] = lookup_table

    pserver_notify_block.append_op(
        type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
    executor.run(pserver_notify_program)


def _endpoints_replacement(program, endpoints):
    ENDPOINT_MAP = "epmap"
    for op in program.global_block().ops:
        if op.has_attr(ENDPOINT_MAP):
            op.set_attr(ENDPOINT_MAP, endpoints)
    program._sync_with_cpp()
    return program


def get_parameter_value(para, executor):
    """
    Get the LoDTensor value of the given parameter.

    Args:
        para(Parameter): The parameter to get value from.
        executor(Executor): The executor to run for retrieving the value.

    Returns:
        numpy.array: The given parameter's values.

    Raises:
        AssertionError: If the `para` is not an instance of Parameter.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            param = fluid.default_main_program().global_block().var('fc.w')
            p = fluid.io.get_parameter_value(param, exe)

    """
    assert is_parameter(para)

    get_program = Program()
    block = get_program.global_block()
    new_var = _clone_var_in_block_(block, para)
    return executor.run(get_program, feed={}, fetch_list=[new_var])[0]


def get_parameter_value_by_name(name, executor, program=None):
    """
    Get the LoDTensor value of a certain parameter by its name.

    Args:
        name(str): The parameter's name.
        executor(Executor): The executor to run for retrieving the value.
        program(Program | None): The program where to find the parameter.
                               If it's set to be None, the function will
                               try to find the parameter in the default
                               main program.

    Returns:
        numpy.array: The parameter's values.

    Raises:
        TypeError: If given `name` is not an instance of basestring.
        TypeError: If the parameter with the given name doesn't exist.
        AssertionError: If there is a varibale named `name` in the
                        given program but it is not a Parameter.

    Examples:
        .. code-block:: python

            exe = fluid.Executor(fluid.CPUPlace())
            p = fluid.io.get_parameter_value('fc.w', exe)
    """
    if program is None:
        program = default_main_program()
    var = program.global_block().var(name)
    return get_parameter_value(var, executor)


def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
    if not slice_vars_and_attrs:
        return

    load_prog = Program()
    load_block = load_prog.global_block()
    need_delete_vars = []

    for var_tuple in slice_vars_and_attrs:
        orig_var = var_tuple[0]
        start = var_tuple[1]
        slice_var = var_tuple[2]
        end = start + slice_var.shape[0]

        orig_var_name = orig_var.name
        orig_var.name = "{}.origin".format(orig_var_name)

        clone_orig_var = load_block.create_var(
            name=orig_var.name,
            type=orig_var.type,
            shape=orig_var.shape,
            dtype=orig_var.dtype,
            persistable=True)

        clone_slice_var = load_block.create_var(
            name=slice_var.name,
            type=slice_var.type,
            shape=slice_var.shape,
            dtype=slice_var.dtype,
            persistable=True)

        load_block.append_op(
            type='load',
            inputs={},
            outputs={'Out': [clone_orig_var]},
            attrs={'file_path': os.path.join(dirname, orig_var_name)})
        load_block.append_op(
            type="slice",
            inputs={'Input': clone_orig_var},
            outputs={'Out': clone_slice_var},
            attrs={'axes': [0],
                   'starts': [start],
                   'ends': [end]})
        need_delete_vars.append(clone_orig_var)

    load_block.append_op(
        type='delete_var',
        inputs={'X': need_delete_vars}, )
    executor.run(load_prog)