framework.py 189.1 KB
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#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# 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
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# 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.

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from __future__ import print_function

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import collections
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from collections import defaultdict
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from collections import Iterable
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import contextlib
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from .wrapped_decorator import signature_safe_contextmanager, wrap_decorator
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import os
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import re
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import traceback
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import six
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import numpy as np
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import subprocess
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import multiprocessing
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import sys
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import logging
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from .. import compat as cpt
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from .proto import framework_pb2
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from . import core
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from . import unique_name
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import paddle.version as fluid_version
import warnings
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__all__ = [
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    'Program',
    'default_startup_program',
    'default_main_program',
    'program_guard',
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    'name_scope',
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    'cuda_places',
    'cpu_places',
    'cuda_pinned_places',
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    'in_dygraph_mode',
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    'is_compiled_with_cuda',
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    'Variable',
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    'ComplexVariable',
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    'load_op_library',
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    'require_version',
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    'device_guard',
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    'set_flags',
    'get_flags',
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]
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EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
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CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()

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_dygraph_tracer_ = None
_dygraph_current_expected_place_ = None
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_current_device = None
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global_prog_seed = 0

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def require_version(min_version, max_version=None):
    """
        Check if the installed version of PaddlePaddle is in [min_version, max_version],
        if the installed version is lower than ``min_version`` or higher than ``max_version``,
        an exception will be thrown, NO returns if the installed version is satisfied.

        Args:
            min_version (str): the minimum version required (like '1.4.0').
            max_version (str, optional): the max version required (like '1.6.0'), default is None,
                meaning any version equal or higher than ``min_version`` is acceptable.

        Returns:
            None.

        Raises:
            TypeError: if the type of ``min_version`` is not str.
            TypeError: if the type of ``max_version`` is not str or type(None).
            ValueError: if the value of ``min_version`` is not in version format.
            ValueError: if the value of ``max_version`` is not in version format or None.
            Exception: if the installed version is lower than ``min_version`` or higher than ``max_version``.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                # any version >= 0.1.0 is acceptable.
                fluid.require_version('0.1.0')

                # if 0.1.0 <= version <= 10.0.0, it is acceptable.
                fluid.require_version(min_version='0.1.0', max_version='10.0.0')
        """
    if not isinstance(min_version, str):
        raise TypeError(
            "The type of 'min_version' in require_version must be str, but received %s."
            % (type(min_version)))

    if not isinstance(max_version, (str, type(None))):
        raise TypeError(
            "The type of 'max_version' in require_version must be str or type(None), but received %s."
            % (type(max_version)))

    check_format = re.match(r'\d+(\.\d+){0,3}', min_version)
    if check_format is None or check_format.group() != min_version:
        raise ValueError(
            "The value of 'min_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
            "like '1.5.2.0', but received %s" % min_version)

    if max_version is not None:
        check_format = re.match(r'\d+(\.\d+){0,3}', max_version)
        if check_format is None or check_format.group() != max_version:
            raise ValueError(
                "The value of 'max_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
                "like '1.5.2.0', but received %s" % max_version)

    version_installed = [
        fluid_version.major, fluid_version.minor, fluid_version.patch,
        fluid_version.rc
    ]
    zero_version = ['0', '0', '0', '0']

    def version_cmp(ver_a, ver_b):
        for i in six.moves.range(len(ver_a)):
            if int(ver_a[i]) > int(ver_b[i]):
                return 1
            elif int(ver_a[i]) < int(ver_b[i]):
                return -1
        return 0

    if version_cmp(version_installed, zero_version) == 0:
        if max_version is not None:
            warnings.warn(
                "PaddlePaddle version in [%s, %s] required, but %s installed. "
                "Maybe you are using a develop version, "
                "please make sure the version is good with your code." %
                (min_version, max_version, fluid_version.full_version))
        else:
            warnings.warn(
                "PaddlePaddle version %s or higher is required, but %s installed, "
                "Maybe you are using a develop version, "
                "please make sure the version is good with your code." %
                (min_version, fluid_version.full_version))
        return

    min_version_split = min_version.split('.')
    min_version_to_check = min_version_split + zero_version[len(
        min_version_split):]

    if max_version is not None:
        max_version_split = max_version.split('.')
        max_version_to_check = max_version_split + zero_version[len(
            max_version_split):]

        if version_cmp(version_installed,
                       max_version_to_check) > 0 or version_cmp(
                           version_installed, min_version_to_check) < 0:
            raise Exception(
                "VersionError: PaddlePaddle version in [%s, %s] required, but %s installed."
                % (min_version, max_version, fluid_version.full_version))
    else:
        if version_cmp(version_installed, min_version_to_check) < 0:
            raise Exception(
                "VersionError: PaddlePaddle version %s or higher is required, but %s installed, "
                "please upgrade your PaddlePaddle to %s or other higher version."
                % (min_version, fluid_version.full_version, min_version))


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def in_dygraph_mode():
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    """
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    This function checks whether the program runs in dynamic graph mode or not.
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    You can enter dynamic graph mode with :ref:`api_fluid_dygraph_guard` api,
    or enable and disable dynamic graph mode with :ref:`api_fluid_dygraph_enable`
    and :ref:`api_fluid_dygraph_disable` api .
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    Returns:
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        bool: Whether the program is running in dynamic graph mode.
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    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            fluid.enable_dygraph()  # Now we are in dygragh mode
            print(fluid.in_dygraph_mode())  # True
            fluid.disable_dygraph()
            print(fluid.in_dygraph_mode())  # False
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    """
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    return _dygraph_tracer_ is not None
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def _dygraph_not_support_(func):
    def __impl__(*args, **kwargs):
        assert not in_dygraph_mode(
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        ), "We don't support %s in imperative mode" % func.__name__
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        return func(*args, **kwargs)

    return __impl__


def _dygraph_only_(func):
    def __impl__(*args, **kwargs):
        assert in_dygraph_mode(
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        ), "We Only support %s in imperative mode, please use fluid.dygraph.guard() as context to run it in imperative Mode" % func.__name__
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        return func(*args, **kwargs)

    return __impl__


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# NOTE(zhiqiu): This decorator is used for the APIs of Variable which is only
# used to make Variable and VarBase has same interfaces, like numpy. Since VarBase is not exposed in our
# official docments, logically, we want to keep VarBase and logically consistent. While, actually,
# in our implementation, there some APIs not supported, like numpy, because Variable contains the desc.
# So, those APIs are listed under class Variable to generate docs only.
# TODO(zhiqiu): We should make VarBase consistent with Variable in future, for example, by inheritting
# same base class. 
def _fake_interface_only_(func):
    def __impl__(*args, **kwargs):
        raise AssertionError(
            "'%s' should be called by imperative Varible in imperative mode, please use fluid.dygraph.guard() as context to run it in imperative mode"
            % func.__name__)

    return __impl__


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dygraph_not_support = wrap_decorator(_dygraph_not_support_)
dygraph_only = wrap_decorator(_dygraph_only_)
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fake_interface_only = wrap_decorator(_fake_interface_only_)
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def _dygraph_tracer():
    return _dygraph_tracer_
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def _current_expected_place():
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    return _dygraph_current_expected_place_
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# TODO(zhiqiu): remove this function.
def _var_base_to_np(var_base):
    """	
    convert VarBase tp numpy	
    	
    Args:	
        var_base(VarBase) : the VarBase to convert	
    Returns (np.ndarray): the np.ndarray contain the value of VarBase	
    """

    warnings.warn(
        "paddle.fluid.framework._var_base_to_np is deprecated, please use var_base.numpy() instead of _var_base_to_np(var_base)."
    )

    return var_base.numpy()


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def _cpu_num():
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    if "CPU_NUM" not in os.environ.keys():
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        if multiprocessing.cpu_count() > 1:
            sys.stderr.write(
                '!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list.\n'
                'CPU_NUM indicates that how many CPUPlace are used in the current task.\n'
                'And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster.\n\n'
                'export CPU_NUM={} # for example, set CPU_NUM as number of physical CPU core which is {}.\n\n'
                '!!! The default number of CPU_NUM=1.\n'.format(
                    multiprocessing.cpu_count(), multiprocessing.cpu_count()))
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        os.environ['CPU_NUM'] = str(1)
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    cpu_num = os.environ.get('CPU_NUM')
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    return int(cpu_num)


def _cuda_ids():
    gpus_env = os.getenv("FLAGS_selected_gpus")
    if gpus_env:
        device_ids = [int(s) for s in gpus_env.split(",")]
    else:
        device_ids = six.moves.range(core.get_cuda_device_count())
    return device_ids
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def is_compiled_with_cuda():
    """
    Whether this whl package can be used to run the model on GPU.

    Returns (bool): support gpu or not.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            support_gpu = fluid.is_compiled_with_cuda()
    """
    return core.is_compiled_with_cuda()


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def cuda_places(device_ids=None):
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    """
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    **Note**:
        For multi-card tasks, please use `FLAGS_selected_gpus` environment variable to set the visible GPU device.
        The next version will fix the problem with `CUDA_VISIBLE_DEVICES` environment variable.

    This function creates a list of :code:`fluid.CUDAPlace` objects.
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    If :code:`device_ids` is None, environment variable of
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    :code:`FLAGS_selected_gpus` would be checked first. For example, if
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    :code:`FLAGS_selected_gpus=0,1,2`, the returned list would
    be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    If :code:`FLAGS_selected_gpus` is not set, all visible
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    gpu places would be returned according to the :code:`CUDA_VISIBLE_DEVICES` environment variable.
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    If :code:`device_ids` is not None, it should be the device
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    ids of GPUs. For example, if :code:`device_ids=[0,1,2]`,
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    the returned list would be 
    [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    
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    Parameters:
        device_ids (list or tuple of int, optional): list of GPU device ids.
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    Returns:
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        list of fluid.CUDAPlace: Created GPU place list.
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    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            cuda_places = fluid.cuda_places()

    """
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    assert core.is_compiled_with_cuda(), \
        "Not compiled with CUDA"
    if device_ids is None:
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        device_ids = _cuda_ids()
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    elif not isinstance(device_ids, (list, tuple)):
        device_ids = [device_ids]
    return [core.CUDAPlace(dev_id) for dev_id in device_ids]


def cpu_places(device_count=None):
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    """
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    This function creates a list of :code:`fluid.CPUPlace` objects, and returns the created list.
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    If :code:`device_count` is None, the device count would
    be determined by environment variable :code:`CPU_NUM`. 
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    If :code:`CPU_NUM` is not set, the default value is 1,
    i.e. CPU_NUM=1.
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    :code:`CPU_NUM` indicates the number of devices used in the current task.
    The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
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    Parameters:
        device_count (int, optional): device number. Default: None.
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    Returns:
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        list of fluid.CPUPlace: Created list of CPU places.
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    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            cpu_places = fluid.cpu_places()
    """

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    if device_count is None:
        device_count = _cpu_num()
    return [core.CPUPlace()] * device_count


def cuda_pinned_places(device_count=None):
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    """
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    This function creates a list of :code:`fluid.CUDAPinnedPlace` objects.
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    If :code:`device_count` is None, the device count would
    be determined by environment variable :code:`CPU_NUM`. 
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    If :code:`CPU_NUM` is not set, the default value is 1,
    i.e. CPU_NUM=1.
    :code:`CPU_NUM` indicates the number of devices used in the current task.
    The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
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    Parameters:
        device_count (int, optional): device number. Default: None.
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    Returns:
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        list of fluid.CUDAPinnedPlace: Created list of CUDA pinned places.
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    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            cuda_pinned_places_cpu_num = fluid.cuda_pinned_places()
            # or
            cuda_pinned_places = fluid.cuda_pinned_places(1)

    """
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    assert core.is_compiled_with_cuda(), \
        "Not compiled with CUDA"
    if device_count is None:
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        device_count = len(_cuda_ids())
    return [core.CUDAPinnedPlace()] * device_count
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class NameScope(object):
    def __init__(self, name="", parent=None):
        self._children = dict()
        self._name = name
        self._parent = parent

    def child(self, prefix):
        if prefix not in self._children:
            new_child = NameScope(prefix, self)
            self._children[prefix] = [new_child]
        else:
            new_child = NameScope(prefix + "_%d" % len(self._children[prefix]),
                                  self)
            self._children[prefix].append(new_child)
        return new_child

    def parent(self):
        return self._parent

    def name(self):
        return self._name


_name_scope = NameScope()


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@signature_safe_contextmanager
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def name_scope(prefix=None):
    """
    Generate hierarchical name prefix for the operators.

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    Note: 
        This should only used for debugging and visualization purpose.
        Don't use it for serious analysis such as graph/program transformations.
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    Args:
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        prefix(str, optional): prefix. Default is none.
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    Examples:
        .. code-block:: python
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          import paddle.fluid as fluid
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          with fluid.name_scope("s1"):
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             a = fluid.data(name='data', shape=[None, 1], dtype='int32')
             b = a + 1
             with fluid.name_scope("s2"):
                c = b * 1
             with fluid.name_scope("s3"):
                d = c / 1
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          with fluid.name_scope("s1"):
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                f = fluid.layers.pow(d, 2.0)
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          with fluid.name_scope("s4"):
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                g = f - 1

          # Op are created in the default main program.  
          for op in fluid.default_main_program().block(0).ops:
              # elementwise_add is created in /s1/
              if op.type == 'elementwise_add':
                  assert op.desc.attr("op_namescope") == '/s1/'
              # elementwise_mul is created in '/s1/s2'
              elif op.type == 'elementwise_mul':
                  assert op.desc.attr("op_namescope") == '/s1/s2/'
              # elementwise_div is created in '/s1/s3'
              elif op.type == 'elementwise_div':
                  assert op.desc.attr("op_namescope") == '/s1/s3/'
              # elementwise_sum is created in '/s4'
              elif op.type == 'elementwise_sub':
                  assert op.desc.attr("op_namescope") == '/s4/'
              # pow is created in /s1_1/
              elif op.type == 'pow':
                  assert op.desc.attr("op_namescope") == '/s1_1/'
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    """
    # TODO(panyx0718): Only [0-9a-z].
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    # in dygraph we don't need namescope since it will cause mem leak
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    if in_dygraph_mode():
        yield
    else:
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        assert prefix, "namescope prefix can not be empty."
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        global _name_scope
        _name_scope = _name_scope.child(prefix)
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        try:
            yield
        finally:
            _name_scope = _name_scope.parent()
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def _full_name_scope():
    global _name_scope
    scope = _name_scope
    name = ""
    while scope:
        name = scope.name() + "/" + name
        scope = scope.parent()
    return name


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def generate_control_dev_var_name():
    import random
    return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
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def grad_var_name(var_name):
    """
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    Returns:
        str: gradient name for a certain var name
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    """
    return var_name + GRAD_VAR_SUFFIX

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def convert_np_dtype_to_dtype_(np_dtype):
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    """
    Convert the data type in numpy to the data type in Paddle
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    Args:
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        np_dtype(np.dtype): the data type in numpy.
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    Returns:
        core.VarDesc.VarType: the data type in Paddle.
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    """
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    dtype = np.dtype(np_dtype)
    if dtype == np.float32:
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        return core.VarDesc.VarType.FP32
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    elif dtype == np.float64:
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        return core.VarDesc.VarType.FP64
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    elif dtype == np.float16:
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        return core.VarDesc.VarType.FP16
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    elif dtype == np.int32:
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        return core.VarDesc.VarType.INT32
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    elif dtype == np.int16:
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        return core.VarDesc.VarType.INT16
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    elif dtype == np.int64:
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        return core.VarDesc.VarType.INT64
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    elif dtype == np.bool:
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        return core.VarDesc.VarType.BOOL
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    elif dtype == np.uint16:
        return core.VarDesc.VarType.INT16
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    elif dtype == np.uint8:
        return core.VarDesc.VarType.UINT8
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    elif dtype == np.int8:
        return core.VarDesc.VarType.INT8
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    else:
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        raise ValueError("Not supported numpy dtype %s" % dtype)
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def dtype_is_floating(dtype):
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    """
    Check the data type is floating or not.
    Args:
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        dtype(np.dtype|core.VarDesc.VarType): data type.
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            Could be numpy format or Paddle format

    Returns(bool): True if data type is a float value

    """
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    if not isinstance(dtype, core.VarDesc.VarType):
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        dtype = convert_np_dtype_to_dtype_(dtype)

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    return dtype in [
        core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
        core.VarDesc.VarType.FP64
    ]
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def _debug_string_(proto, throw_on_error=True):
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    """
    Get the debug string of a protobuf message. The message could be not
    initialized.
    Args:
        proto(google.protobuf.message.Message): The protobuf message
        throw_on_error(bool): True if raise an error when the protobuf message
            is not initialized.

    Returns(str): The debug string of the protobuf message

    """
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    error_fields = list()
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    if not proto.IsInitialized(error_fields) and throw_on_error:
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        raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
                         format(error_fields, proto))
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    return proto.__str__()


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def _varbase_creator(type=core.VarDesc.VarType.LOD_TENSOR,
                     name=None,
                     shape=None,
                     dtype=None,
                     persistable=None,
                     **kwargs):
    if dtype is not None:
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)

    return core.VarBase(dtype if dtype else core.VarDesc.VarType.FP32,
                        list(shape) if shape else [], name, type
                        if type else core.VarDesc.VarType.LOD_TENSOR, True
                        if persistable else False)


class VariableMetaClass(type):
    @classmethod
    def __instancecheck__(cls, instance):
        t = type(instance)
        if in_dygraph_mode():
            return issubclass(t, core.VarBase)
        else:
            return issubclass(t, Variable)


class ParameterMetaClass(VariableMetaClass):
    @classmethod
    def __instancecheck__(cls, instance):
        t = type(instance)
        if in_dygraph_mode():
            return issubclass(t, ParamBase)
        else:
            return issubclass(t, Parameter)


def _getitem_impl_(var, item):
    """
    Slice the variable.

    Args:
        item(int/slice/tuple) : the index.

    Returns:
        Sliced variable
    """

    if not isinstance(item, tuple):
        item = [item]

    decrease_axis = []
    slice_axis = []
    slice_start = []
    slice_end = []
    slice_step = []
    use_strided_slice = False
    reverse_axis = []

    def fill_constant(shape, value, force_cpu=False, out=None):
        var.block.append_op(
            type='fill_constant',
            inputs={},
            outputs={'Out': [out]},
            attrs={
                'shape': shape,
                'dtype': out.dtype,
                'value': float(value),
                'force_cpu': force_cpu
            },
            stop_gradient=True)
        out.stop_gradient = True
        return out

    for dim, slice_item in enumerate(item):
        if isinstance(slice_item, slice):
            start = slice_item.start
            end = slice_item.stop
            step = slice_item.step

            if start is None and end is None and step is None:
                continue

            if step is None:
                step = 1

            if start is None and end is None:
                assert (step == -1)
                reverse_axis.append(dim)
                continue

            if start is None:
                start = 0

            if end is None:
                end = 10000000

            if step != 1:
                use_strided_slice = True

            slice_axis.append(dim)
            slice_start.append(start)
            slice_end.append(end)
            slice_step.append(step)
        else:
            decrease_axis.append(dim)
            slice_axis.append(dim)
            slice_start.append(slice_item)
            slice_step.append(1)
            if isinstance(slice_item, Variable):
                temp_1 = var.block.create_var(dtype='int32')
                fill_constant([1], 1, force_cpu=True, out=temp_1)
                temp_end = var.block.create_var(dtype='int32')
                var.block.append_op(
                    type='elementwise_add',
                    inputs={'X': slice_item,
                            'Y': temp_1},
                    outputs={'Out': temp_end},
                    attrs={'axis': -1})
                slice_end.append(temp_end)
            else:
                slice_end.append(slice_item + 1
                                 if slice_item != -1 else 10000000)

    def contain_var(one_list):
        for ele in one_list:
            if isinstance(ele, Variable):
                return True
        return False

    def get_new_list_tensor(old_list):
        new_list_tensor = []
        for dim in old_list:
            if isinstance(dim, Variable):
                dim.stop_gradient = True
                new_list_tensor.append(dim)
            else:
                assert (isinstance(dim, int))
                temp_out = var.block.create_var(dtype='int32')
                fill_constant([1], dim, force_cpu=True, out=temp_out)
                new_list_tensor.append(temp_out)
        return new_list_tensor

    inputs = {'Input': [var]}
    attrs = {
        'axes': slice_axis,
        'starts': [],
        'ends': [],
        'decrease_axis': decrease_axis
    }
    if (use_strided_slice == True):
        attrs['strides'] = []
    infer_flags = list(1 for i in range(len(slice_axis)))
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    # starts
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    if contain_var(slice_start):
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        inputs['StartsTensorList'] = get_new_list_tensor(slice_start)
        for i, dim in enumerate(slice_start):
            if isinstance(dim, Variable):
                attrs['starts'].append(-1)
                infer_flags[i] = -1
            else:
                attrs['starts'].append(dim)
    else:
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        attrs['starts'] = slice_start

    # ends
    if contain_var(slice_end):
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        inputs['EndsTensorList'] = get_new_list_tensor(slice_end)
        for i, dim in enumerate(slice_end):
            if isinstance(dim, Variable):
                attrs['ends'].append(-1)
                infer_flags[i] = -1
            else:
                attrs['ends'].append(dim)
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    else:
        attrs['ends'] = slice_end

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    # strides
    if use_strided_slice == True:
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        if contain_var(slice_step):
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            inputs['StridesTensorList'] = get_new_list_tensor(slice_step)
            for i, dim in enumerate(slice_step):
                if isinstance(dim, Variable):
                    attrs['strides'].append(-1)
                    infer_flags[i] = -1
                else:
                    attrs['strides'].append(dim)
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        else:
            attrs['strides'] = slice_step
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    # infer_flags
    attrs['infer_flags'] = infer_flags

    out = var
    if use_strided_slice == False and len(slice_axis) > 0:
        # append slice_op here
        slice_out_var = var.block.create_var(
            name=unique_name.generate_with_ignorable_key(var.name + "_slice"),
            dtype=var.dtype)

        var.block.append_op(
            type="slice",
            inputs=inputs,
            outputs={'Out': [slice_out_var]},
            attrs=attrs)

        out = slice_out_var
    elif use_strided_slice == True and len(slice_axis) > 0:
        strided_slice_out_var = var.block.create_var(
            name=unique_name.generate_with_ignorable_key(var.name +
                                                         "_strided_slice"),
            dtype=var.dtype)
        var.block.append_op(
            type="strided_slice",
            inputs=inputs,
            outputs={'Out': [strided_slice_out_var]},
            attrs=attrs)

        out = strided_slice_out_var

    if len(reverse_axis) > 0:
        reverse_out_var = var.block.create_var(
            name=unique_name.generate_with_ignorable_key(var.name +
                                                         "_slice_reverse"),
            dtype=var.dtype)
        var.block.append_op(
            type="reverse",
            inputs={'X': out},
            outputs={'Out': [reverse_out_var]},
            attrs={'axis': reverse_axis})

        out = reverse_out_var

    return out


@six.add_metaclass(VariableMetaClass)
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class Variable(object):
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    """
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    **Notes**:
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        **The constructor of Variable should not be invoked directly.**
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        **In Static Graph Mode: Please use** `Block.create_var` **to create a Static variable which has no data until being feed.**

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        **In Dygraph Mode: Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph variable with real data**

    In Fluid, every input and output of an OP is a variable. In most
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    cases, variables are used for holding different kinds of data or training
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    labels. A variable belongs to a :ref:`api_guide_Block_en` . All variable has its own name and
    two variables in different :ref:`api_guide_Block_en` could have the same name.
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    There are many kinds of variables. Each kind of them has its own attributes
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    and usages. Please refer to the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details.
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    Most of a Variable's member variables can be set to be None. It mean
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    it is not available or will be specified later.
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    Examples:
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        In Static Graph Mode:

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        .. code-block:: python

854
            import paddle.fluid as fluid
855
            cur_program = fluid.Program()
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            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
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        In `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_  Mode:
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        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            with fluid.dygraph.guard():
                new_variable = fluid.dygraph.to_variable(np.arange(10))

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    """

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    def __init__(self,
                 block,
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                 type=core.VarDesc.VarType.LOD_TENSOR,
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                 name=None,
                 shape=None,
                 dtype=None,
                 lod_level=None,
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                 capacity=None,
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                 persistable=None,
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                 error_clip=None,
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                 stop_gradient=False,
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                 is_data=False,
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                 need_check_feed=False,
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                 belong_to_optimizer=False,
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                 **kwargs):
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        self.block = block
        if name is None:
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            name = unique_name.generate('_generated_var')
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        if dtype is not None:
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            if not isinstance(dtype, core.VarDesc.VarType):
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                dtype = convert_np_dtype_to_dtype_(dtype)
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        self.belong_to_optimizer = belong_to_optimizer

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        self.error_clip = error_clip

        is_new_var = False
        name = cpt.to_text(name)
        self.desc = self.block.desc.find_var(cpt.to_bytes(name))
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        if self.desc is None:
            self.desc = self.block.desc.var(cpt.to_bytes(name))
            is_new_var = True
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        if is_new_var:
            self.desc.set_type(type)
        elif self.desc.type() != type:
            raise ValueError("Variable {0} has been created before. The "
                             "previous type is {1}; the new type is {2}. They"
                             " are not matched".format(self.name,
                                                       self.desc.type(), type))
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        if shape is not None:
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            if is_new_var:
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                self.desc.set_shape(shape)
            else:
                old_shape = self.shape
                shape = tuple(shape)
                if shape != old_shape:
                    raise ValueError(
                        "Variable {0} has been created before. the previous "
                        "shape is {1}; the new shape is {2}. They are not "
                        "matched.".format(self.name, old_shape, shape))
        if dtype is not None:
            if is_new_var:
                self.desc.set_dtype(dtype)
            else:
                old_dtype = self.dtype
                if dtype != old_dtype:
                    raise ValueError("Variable {0} has been created before. "
                                     "The previous data type is {1}; the new "
                                     "data type is {2}. They are not "
                                     "matched.".format(self.name, old_dtype,
                                                       dtype))

        if lod_level is not None:
            if is_new_var:
                self.desc.set_lod_level(lod_level)
            else:
                if lod_level != self.lod_level:
                    raise ValueError("Variable {0} has been created before. "
                                     "The previous lod_level is {1}; the new "
                                     "lod_level is {2}. They are not "
                                     "matched".format(self.name, self.lod_level,
                                                      lod_level))
        if persistable is not None:
            if is_new_var:
                self.desc.set_persistable(persistable)
            else:
                if persistable != self.persistable:
                    raise ValueError(
                        "Variable {0} has been created before."
                        "The previous persistable is {1}; the new "
                        "persistable is {2}. They are not matched".format(
                            self.name, self.persistable, persistable))
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        if need_check_feed and is_new_var:
            self.desc.set_need_check_feed(need_check_feed)
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        if capacity is not None:
            if is_new_var:
                self.desc.set_capacity(capacity)
            else:
                # TODO(abhinavarora) : Compare with set capacity once,
                # get_capacity is implemented
                pass
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        self.block.vars[name] = self
        self.op = None
        self._stop_gradient = stop_gradient
        self.is_data = is_data
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    @fake_interface_only
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    def detach(self):
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
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        Returns a new Variable, detached from the current graph.
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983
        Returns:
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             ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
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                from paddle.fluid.dygraph import Linear
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                import numpy as np

                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                with fluid.dygraph.guard():
997
                    linear = Linear(32, 64)
998
                    data = to_variable(data)
999
                    x = linear(data)
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                    y = x.detach()

        """
1003
        pass
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    @fake_interface_only
1006
    def numpy(self):
1007
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
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        Returns a numpy array shows the value of current :ref:`api_guide_Variable_en`
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        Returns:
            ndarray: The numpy value of current Variable.

        Returns type:
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            ndarray: dtype is same as current Variable
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
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                from paddle.fluid.dygraph import Linear
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                import numpy as np

                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                with fluid.dygraph.guard():
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                    linear = Linear(32, 64)
1030
                    data = to_variable(data)
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                    x = linear(data)
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                    print(x.numpy())

        """
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        pass
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    @fake_interface_only
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    def set_value(self, value):
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
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        Set a new value for this Variable.

        Args:
            value (Variable|np.ndarray): the new value.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
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                from paddle.fluid.dygraph import Linear
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                import numpy as np

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                data = np.ones([3, 1024], dtype='float32')
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                with fluid.dygraph.guard():
1058
                    linear = fluid.dygraph.Linear(1024, 4)
1059
                    t = to_variable(data)
1060
                    linear(t)  # call with default weight
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                    custom_weight = np.random.randn(1024, 4).astype("float32")
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                    linear.weight.set_value(custom_weight)  # change existing weight
                    out = linear(t)  # call with different weight
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        """
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        pass
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    @fake_interface_only
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    def backward(self, backward_strategy=None):
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        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
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        Run backward of current Graph which starts from current Variable

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        Args:
            backward_strategy( :ref:`api_fluid_dygraph_BackwardStrategy` ): The Backward Strategy to run backward
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        Returns:
            NoneType: None
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
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                        # if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since
                        # there is no one need gradient on it.
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                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)

        """
1104
        pass
1105

1106
    @fake_interface_only
1107
    def gradient(self):
1108
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
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        Get the Gradient of Current Variable

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        Returns:
1115
            ndarray or tuple of ndarray: if Variable's type is LoDTensor, return numpy value of the gradient of current Variable, if Variable's type is SelectedRows, return tuple of ndarray, first element of tuple is numpy value of the gradient of current Variable, second element of tuple is numpy value of the rows of current Variable.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

1123
                # example1: return ndarray
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                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)
                    print(loss2.gradient())

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                # example2: return tuple of ndarray
                with fluid.dygraph.guard():
                    embedding = fluid.dygraph.Embedding(
                        size=[20, 32],
                        param_attr='emb.w',
                        is_sparse=True)
                    x_data = np.arange(12).reshape(4, 3).astype('int64')
                    x_data = x_data.reshape((-1, 3, 1))
                    x = fluid.dygraph.base.to_variable(x_data)
                    out = embedding(x)
                    out.backward()
                    print(embedding.weight.gradient())

1151
        """
1152
        pass
1153

1154
    @fake_interface_only
1155
    def clear_gradient(self):
1156
        """
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        **Notes**:
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            **1. This API is ONLY available in Dygraph mode**
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            **2. Use it only Variable has gradient, normally we use this for Parameters since other temporal Variable will be deleted by Python's GC**
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        Clear  (set to ``0`` ) the Gradient of Current Variable
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        Returns:  None

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)
                    print(loss2.gradient())
                    loss2.clear_gradient()
                    print("After clear {}".format(loss2.gradient()))

        """
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        pass
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    def __str__(self):
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        return self._to_readable_code()

    def _to_readable_code(self):
        """
        Get readable debug string of Variable.

        .. note::
            If you want to get the debug string in protobuf format,
            please use :code:`to_string` method.

        Returns:
            string: The formatted Variable string.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                cur_program = fluid.Program()
                cur_block = cur_program.current_block()
                new_variable = cur_block.create_var(name="X",
                                                    shape=[-1, 23, 48],
                                                    dtype='float32')
                print(new_variable._to_readable_code())
        """
        if self.type == core.VarDesc.VarType.SELECTED_ROWS or self.type == core.VarDesc.VarType.LOD_TENSOR:
            var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})".\
                format(i="{", e="}", name=self.name, type=self.type, shape=self.shape, dtype=self.dtype)
        else:
            var_str = "{name} : fluid.{type})".\
                format(i="{", e="}", name=self.name, type=self.type)

        if type(self) == Parameter:
            if self.trainable:
                var_str = "trainable param " + var_str
            else:
                var_str = "param " + var_str
        else:
            var_str = "var " + var_str

        if self.persistable:
            var_str = "persist " + var_str

        return var_str
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    def to_string(self, throw_on_error, with_details=False):
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        """
        Get debug string.

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        Args:

            throw_on_error (bool): True if raise an exception when self is not initialized.

            with_details (bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default value is False;
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        Returns:
            str: The debug string.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
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                cur_program = fluid.Program()
                cur_block = cur_program.current_block()
                new_variable = cur_block.create_var(name="X",
                                                    shape=[-1, 23, 48],
                                                    dtype='float32')
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                print(new_variable.to_string(True))
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                print("=============with detail===============")
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                print(new_variable.to_string(True, True))
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        """
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        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
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        protostr = self.desc.serialize_to_string()
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        proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
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        res_str = _debug_string_(proto, throw_on_error)
        if with_details:
            additional_attr = ("error_clip", "stop_gradient")
            for attr_name in additional_attr:
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                res_str += "%s: %s\n" % (attr_name,
                                         cpt.to_text(getattr(self, attr_name)))

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        return res_str
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    __repr__ = __str__

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    @property
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    def stop_gradient(self):
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        """
        Indicating if we stop gradient from current Variable

        **Notes: This Property has default value as** ``True`` **in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, while Parameter's default value is False. However, in Static Graph Mode all Variable's default stop_gradient value is** ``False``

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            with fluid.dygraph.guard():
                value0 = np.arange(26).reshape(2, 13).astype("float32")
                value1 = np.arange(6).reshape(2, 3).astype("float32")
                value2 = np.arange(10).reshape(2, 5).astype("float32")
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                linear = fluid.Linear(13, 5, dtype="float32")
                linear2 = fluid.Linear(3, 3, dtype="float32")
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                a = fluid.dygraph.to_variable(value0)
                b = fluid.dygraph.to_variable(value1)
                c = fluid.dygraph.to_variable(value2)
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                out1 = linear(a)
                out2 = linear2(b)
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                out1.stop_gradient = True
                out = fluid.layers.concat(input=[out1, out2, c], axis=1)
                out.backward()

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                assert linear.weight.gradient() is None
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                assert (out1.gradient() == 0).all()
        """
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        return self._stop_gradient
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    @stop_gradient.setter
    def stop_gradient(self, s):
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        self._stop_gradient = s
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    @property
    def persistable(self):
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        """
        Indicating if we current Variable should be long-term alive


        **Notes: This Property will be deprecated and this API is just to help user understand concept**

            **1. All Variable's persistable is** ``False`` **except Parameters.**

            **2. In** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, this property should not be changed**

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("persistable of current Var is: {}".format(new_variable.persistable))
        """
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        return self.desc.persistable()
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    @persistable.setter
    def persistable(self, p):
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        self.desc.set_persistable(p)
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    @property
    def name(self):
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        """
        Indicating name of current Variable

        **Notes: If it has two or more Varaible share the same name in the same** :ref:`api_guide_Block_en` **, it means these Variable will share content in no-** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode. This is how we achieve Parameter sharing**

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("name of current Var is: {}".format(new_variable.name))
        """
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        return cpt.to_text(self.desc.name())
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    @property
    def grad_name(self):
        """
        Indicating name of the gradient Variable of current Variable.

        **Notes: This is a read-only property. It simply returns name of
          gradient Variable from a naming convention but doesn't guarantee
          the gradient exists.**
       
        Examples:
          .. code-block:: python

          import paddle.fluid as fluid

          x = fluid.data(name="x", shape=[-1, 23, 48], dtype='float32')
          print(x.grad_name) # output is "x@GRAD"

        """
        return self.name + "@GRAD"

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    @name.setter
    def name(self, new_name):
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        self.desc.set_name(new_name)
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    @property
    def shape(self):
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        """
        Indicating shape of current Variable

        **Notes: This is a read-only property**

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("shape of current Var is: {}".format(new_variable.shape))

        """
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        # convert to tuple, make it as same as numpy API.
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        return tuple(self.desc.shape())
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    @property
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    def dtype(self):
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        """
        Indicating data type of current Variable

        **Notes: This is a read-only property**

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("Dtype of current Var is: {}".format(new_variable.dtype))
        """
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        return self.desc.dtype()
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    @property
    def lod_level(self):
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        """
        Indicating ``LoD`` info of current Variable, please refer to  :ref:`api_fluid_LoDTensor_en` to check the meaning
        of ``LoD``

        **Notes**:

            **1. This is a read-only property**

            **2. Don't support this property in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, it's value should be** ``0(int)``

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("LoD Level of current Var is: {}".format(new_variable.lod_level))
        """
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        if self.type == core.VarDesc.VarType.SELECTED_ROWS:
            raise Exception("SelectedRows DO NOT supprt lod")

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        return self.desc.lod_level()
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    @property
    def type(self):
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        """
        Indicating Type of current Variable

        **Notes: This is a read-only property**

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("Type of current Var is: {}".format(new_variable.type))
        """
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        return self.desc.type()
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    def _set_error_clip(self, error_clip):
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        """
        Set the error_clip.

        Args:
            error_clip(BaseErrorClipAttr) : The new error_clip.

        Returns:
            None
        """
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        self.error_clip = error_clip

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    def _set_info(self, key, value):
        """
        Set key-value information for this variable.

        Args:
            key(str): Key for this information.
            value(object): The value associated to the key.

        Returns: 
            None
        """
        if not hasattr(self, "_info"):
            self._info = {}
        self._info[key] = value

    def _get_info(self, key):
        """
        Get the information of this variable corresponding to key.

        Args:
            key(str): Key for this information.

        Returns: 
            object
        """
        if hasattr(self, "_info") and key in self._info:
            return self._info[key]
        return None

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    def _slice_indices(self, slice, length):
        """
        Reference implementation for the slice.indices method.
        """
        # Compute step and length as integers.
        step = 1 if slice.step is None else slice.step

        # Raise ValueError for negative length or zero step.
        if length < 0:
            raise ValueError("length should not be negative")
        if step == 0:
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            raise ValueError("slice step can not be zero")
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        # Find lower and upper bounds for start and stop.
        lower = -1 if step < 0 else 0
        upper = length - 1 if step < 0 else length

        # Compute start.
        if slice.start is None:
            start = upper if step < 0 else lower
        else:
            start = slice.start
            start = max(start + length, lower) if start < 0 else min(start,
                                                                     upper)

        # Compute stop.
        if slice.stop is None:
            stop = lower if step < 0 else upper
        else:
            stop = slice.stop
            stop = max(stop + length, lower) if stop < 0 else min(stop, upper)

        return start, stop, step

    def _detectEllipsis(self, item):
        has_ellipsis = False
        start = 0
        end = len(self.shape)
        for index, o in enumerate(item):
            if o is Ellipsis:
                if has_ellipsis:
                    raise ValueError("Index can have one ellipsis only.")
                has_ellipsis = True
                start = index
            else:
                if has_ellipsis:
                    end = index
        return has_ellipsis, start, end

    def _reconstructSliceinfo(self, item):
        has_ellipsis, start, end = self._detectEllipsis(item)
        if has_ellipsis:
            newitem = []
            for i in range(start):
                newitem.append(item[i])
            for i in range(start, end):
                newitem.append(slice(None, None, None))
            for i in range(end, len(item)):
                newitem.append(item[i])
            return newitem
        else:
            return None

    def _detectContinuesSlice(self, item):
        starts = []
        ends = []
        for index, o in enumerate(item):
            if isinstance(o, int):
                start = int(o)
                if (index > 0 and index >= self.shape[index]) \
                        or (index < 0 and (index + self.shape[index]) < 0):
                    raise IndexError("invalid index")
                start = max(start + self.shape[index], 0) if start < 0 else min(
                    start, self.shape[index])
                starts.append(start)
                ends.append(start + 1)
            elif isinstance(o, slice):
                start, stop, step = self._slice_indices(o, self.shape[index])
                if step == 1 or step == -1:
                    starts.append(start)
                    ends.append(stop)
                else:
                    return False, None
            else:
                raise IndexError("Valid index accept int or slice or ellipsis")
        return True, [starts, ends]

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    def _cloneVar(self, copy=False):
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        if not copy:
            return self.block.create_var(
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                name=unique_name.generate_with_ignorable_key(self.name),
                dtype=self.dtype)
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        else:
            return self

    def _sliceVar(self, axes, starts, ends):
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        new_var = self._cloneVar()
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        self.block.append_op(
            type="slice",
            inputs={'Input': [self]},
            outputs={'Out': [new_var]},
            attrs={'axes': axes,
                   'starts': starts,
                   'ends': ends})
        return new_var

    def _concatVar(self, inputs, axis):
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        new_var = self._cloneVar()
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        self.block.append_op(
            type="concat",
            inputs={'X': inputs},
            outputs={'Out': [new_var]},
            attrs={'axis': axis, })
        return new_var

    def _sliceAndConcatVar(self, item, axis):
        if isinstance(item, slice):
            if self.shape[axis] < 0:
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                return self._cloneVar(True)
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            start, stop, step = self._slice_indices(item, self.shape[axis])
            if step == 1:
                return self._sliceVar([axis], [start], [stop])
            else:
                vars = []
                if step > 0:
                    while start < stop:
                        vars.append(
                            self._sliceVar([axis], [start], [start + 1]))
                        start += step
                else:
                    while start > stop:
                        vars.append(
                            self._sliceVar([axis], [start], [start + 1]))
                        start += step
                return self._concatVar(vars, axis)
        elif isinstance(item, int):
            if self.shape[axis] < 0:
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                return self._cloneVar(True)
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            index = int(item)
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            if (index > 0 and index >= self.shape[axis]) \
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                    or (index < 0 and (index + self.shape[axis]) < 0):
                raise IndexError("invalid index")
            return self._sliceVar([axis], [index], [index + 1])
        else:
            raise IndexError("Valid index accept int or slice or tuple")

    def __getitem__(self, item):
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        return _getitem_impl_(self, item)
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def get_all_op_protos():
    """
    Get all registered op proto from PaddlePaddle C++ end.
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    Returns:
       list: list of OpProto.
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    """
    protostrs = core.get_all_op_protos()
    ret_values = []
    for pbstr in protostrs:
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        op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
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        ret_values.append(op_proto)
    return ret_values


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class ComplexVariable(object):
    """
    The Variable defined on the complex number domain. It contains two common 
    real number Variables as its members, :attr:`real` and :attr:`imag` 
    holding the real part and imaginary part of complex numbers respectively.
    
    **Notes**:
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        **The constructor of ComplexVariable should not be invoked directly.**
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        **Only support dygraph mode at present. Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph ComplexVariable with complex number data.**
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    Args:
        real (Variable): The Variable holding real-part data.
        imag (Variable): The Variable holding imaginery-part data.
    
    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

            a = np.array([1.0+2.0j, 0.2])
            with fluid.dygraph.guard():
                var = fluid.dygraph.to_variable(a, name="new_var")
                print(var.name, var.dtype, var.shape)
                # ({'real': u'new_var.real', 'imag': u'new_var.imag'}, 'complex128', [2L]) 
                print(var.numpy())
                # [1. +2.j 0.2+0.j]
    """

    def __init__(self, real, imag):
        assert real.shape == imag.shape, "The real part and imaginary part " \
            "of a ComplexVariable should have the same shape!"
        assert real.dtype == imag.dtype, "The real part and imaginary part " \
            "of a ComplexVariable should have the same data type!"

        self.real = real
        self.imag = imag
        if self.real.dtype in [
                core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32
        ]:
            self._dtype = "complex64"
        else:
            self._dtype = "complex128"
        self._shape = self.real.shape

    @property
    def dtype(self):
        return self._dtype

    @property
    def shape(self):
        return self._shape

    @property
    def name(self):
        return {"real": self.real.name, "imag": self.imag.name}

    @name.setter
    def name(self, name):
        # rename
        if isinstance(name, str):
            self.real.name = name + ".real"
            self.imag.name = name + ".imag"
        elif (isinstance(name, tuple) or isinstance(name,
                                                    list)) and len(name) == 2:
            self.real.name, self.imag.name = name[0], name[1]
        else:
            raise ValueError(
                "An invalid name assigned to the ComplexVariable, "
                "which must be a string, or a tuple or a list with length 2!")

    def numpy(self):
        return self.real.numpy() + 1j * self.imag.numpy()

    def __str__(self):
        return "REAL: " + self.real.__str__() + "IMAG: " + self.imag.__str__()

    __repr__ = __str__


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class OpProtoHolder(object):
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    """
    A global variable to hold all OpProtos from C++ as a map
    """

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    @classmethod
    def instance(cls):
        if not hasattr(cls, '_instance'):
            cls._instance = cls()
        return cls._instance

    def __init__(self):
        assert not hasattr(
            self.__class__,
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            '_instance'), 'Please use `instance()` to get OpProtoHolder object!'
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        op_protos = get_all_op_protos()
        self.op_proto_map = {}
        for proto in op_protos:
            self.op_proto_map[proto.type] = proto

    def get_op_proto(self, type):
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        """
        Get OpProto by a type string.
        Args:
            type(str): The type that operator registered in C++ side.

        Returns(framework_pb2.OpProto): The OpProto

        """
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        if type not in self.op_proto_map:
            raise ValueError("Operator \"%s\" has not been registered." % type)
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        return self.op_proto_map[type]

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    def update_op_proto(self):
        op_protos = get_all_op_protos()
        for proto in op_protos:
            if proto.type not in self.op_proto_map:
                self.op_proto_map[proto.type] = proto

1805 1806 1807 1808
    @staticmethod
    def generated_op_attr_names():
        return {
            core.op_proto_and_checker_maker.kOpRoleAttrName(),
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            core.op_proto_and_checker_maker.kOpRoleVarAttrName(),
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            core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
1811 1812
            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName(),
            core.op_proto_and_checker_maker.kOpDeviceAttrName()
1813 1814
        }

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class Operator(object):
1817
    """
1818 1819 1820 1821 1822 1823 1824
    In Fluid, all the operation are represented by Operator, and Operator
    is regarded as a build in an instruction of a Block. Users can use the
    build in instructions to describe their neural network.

    Args:
        block(Block): The block has the current operator.
        desc(core.OpDesc): The protobuf description of Operator.
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        type(str): The type of operator. Default None.
1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845
        inputs(dict): The input of this Operator. it is a dictionary, for every
            element, key is the input parameter name, and value is a list of
            variables. Default None.
        outputs(dict): The output of this Operator. it is a dictionary, for
            every element, key is the input parameter name, and value is a list
            of variables. Default None.
        attrs(dict): The attributes of this Operator. it is a dictionary, for
            every element, key is attribute name, and value is the attribute value.
            The attribute type should be as same as the type registered in C++ side.
            Default None.

    Returns:
        Operator: The initialized Operator.

    Raises:
        ValueError: If the passed input, output and attrs doesn't match the
            initializing Operator's that registered in C++ side.

    Notes:
        The constructor of operator should not be invoked directly. Use
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        Block.append_op or Block._prepend_op instead.
1847 1848 1849 1850

    Examples:
        .. code-block:: python

1851
            import paddle.fluid as fluid
1852
            cur_program = fluid.Program()
1853 1854 1855 1856 1857
            cur_block = cur_program.current_block()
            # var1 += var2 + var3
            cur_block.append_op(type="sum",
                                inputs={"X": [var1, var2, var3]},
                                outputs={"Out": [var1]})
1858
    """
1859
    OP_WITHOUT_KERNEL_SET = {
1860 1861
        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
1862 1863
        'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
        'gen_nccl_id', 'c_gen_nccl_id', 'c_comm_init', 'c_sync_calc_stream',
1864
        'c_sync_comm_stream'
1865
    }
1866

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    def __init__(self,
                 block,
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                 desc,
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                 type=None,
                 inputs=None,
                 outputs=None,
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                 attrs=None):
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        if in_dygraph_mode():
1875 1876
            if type is None:
                raise ValueError(
1877
                    "`type` to initialized an Operator can not be None.")
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            self._type = type
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            self.attrs = attrs if attrs else {}
1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893
        else:
            self.block = block
            self.desc = desc
            # note: not add self.attrs here:
            # https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
            op_attrs = attrs
            if op_attrs is None:
                op_attrs = dict()
            del attrs

            op_maker = core.op_proto_and_checker_maker

            if op_maker.kOpRoleAttrName() not in op_attrs:
                op_attrs[op_maker.kOpRoleAttrName(
1894
                )] = self.block.program._op_role
1895 1896 1897

            role_var_name = op_maker.kOpRoleVarAttrName()
            if len(self.block.program.
1898 1899
                   _op_role_var) != 0 and role_var_name not in op_attrs:
                op_attrs[role_var_name] = self.block.program._op_role_var
1900 1901 1902 1903 1904 1905 1906 1907

            if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
                del op_attrs[role_var_name]

            if len(self.desc.type()) != 0:
                return
            if type is None:
                raise ValueError(
1908
                    "`type` to initialized an Operator can not be None.")
1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919
            else:
                callstack_var_name = op_maker.kOpCreationCallstackAttrName()
                op_attrs[callstack_var_name] = list(
                    reversed(traceback.format_stack()))[1:]

            self.desc.set_type(type)
            proto = OpProtoHolder.instance().get_op_proto(type)

            namescope_var_name = op_maker.kOpNameScopeAttrName()
            op_attrs[namescope_var_name] = _full_name_scope()

1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937
            # set device for op with kernels, give warning for op without kernels
            # when force_cpu and device_guard are used at the same time, a warning will be given.
            # TODO(zhangting2020): when force_cpu is removed, clear warning below.
            if _current_device is not None:
                if self._has_kernel(type):
                    op_device = op_maker.kOpDeviceAttrName()
                    op_attrs[op_device] = _current_device
                else:
                    warnings.warn("The Op(%s) is not support to set device." %
                                  type)
                if 'force_cpu' in op_attrs:
                    if (type is 'less_than' and op_attrs['force_cpu'] != None
                        ) or op_attrs['force_cpu'] != False:
                        warnings.warn(
                            "The Attr(force_cpu) of Op(%s) will be deprecated in the future, "
                            "please use 'device_guard' instead. 'device_guard' has higher priority when they are "
                            "used at the same time." % type)

1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957
            def find_name(var_list, name):
                for var_name in var_list:
                    if var_list[var_name] is not None and var_name == name:
                        return True
                return False

            if inputs is not None:
                for in_proto in proto.inputs:
                    found = find_name(inputs, in_proto.name)
                    assert found or in_proto.dispensable, "Input {} not found".format(
                        in_proto.name)
                    if found:
                        in_args = inputs[in_proto.name]
                        if not isinstance(in_args, list):
                            in_args = [in_args]
                        if not in_proto.duplicable and len(in_args) > 1:
                            raise ValueError(
                                "Input %s expects only one input, but %d are given."
                                % (in_proto.name, len(in_args)))
                        in_arg_names = []
1958
                        for index, arg in enumerate(in_args):
1959 1960 1961 1962
                            if isinstance(arg, six.string_types):
                                in_arg_names.append(arg)
                            elif isinstance(arg, six.binary_type):
                                in_arg_names.append(arg.decode())
1963
                            elif isinstance(arg, Variable):
1964
                                in_arg_names.append(cpt.to_text(arg.name))
1965
                            else:
1966 1967 1968 1969
                                raise TypeError(
                                    "The type of '%s' in operator %s should be "
                                    "one of [basestring(), str, Varibale] in python2, "
                                    "or one of [str, bytes, Variable] in python3."
1970 1971
                                    "but received : %s" %
                                    (in_proto.name, type, arg))
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                        self.desc.set_input(in_proto.name, in_arg_names)
                    else:
                        self.desc.set_input(in_proto.name, [])

            if outputs is not None:
                for m in proto.outputs:
                    if (m.name not in outputs) and m.dispensable:
                        continue
                    if not ((m.name in outputs) or m.dispensable):
                        raise ValueError(("Incorrect setting for output(s) of "
                                          "operator \"%s\", should set: [%s].")
                                         % (type, m.name))
                for out_proto in proto.outputs:
                    if out_proto.name not in outputs:
                        continue
                    out_args = outputs[out_proto.name]
                    if not isinstance(out_args, list):
                        out_args = [out_args]
                    if not out_proto.duplicable and len(out_args) > 1:
                        raise ValueError(
                            "Output %s expects only one output, but %d are given."
                            % (out_proto.name, len(out_args)))
                    out_arg_names = []
                    for arg in out_args:
                        out_arg_names.append(cpt.to_text(arg.name))
                        # TODO(minqiyang): could we remove variable's op in static mode?
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                        if not in_dygraph_mode():
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                            arg.op = self
                    self.desc.set_output(out_proto.name, out_arg_names)

            if op_attrs is not None:
                if not isinstance(op_attrs, dict):
                    raise TypeError("'attrs' should be a dict.")
                for attr in proto.attrs:
                    attr_name = attr.name
                    if (attr_name not in op_attrs) or (
                            op_attrs[attr_name] is None):
                        continue
                    attr_val = op_attrs[attr_name]
                    self._update_desc_attr(attr_name, attr_val)

            self.desc.check_attrs()
            if self._has_kernel(type):
                self.desc.infer_var_type(self.block.desc)
                self.desc.infer_shape(self.block.desc)

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    def _has_kernel(self, op_type):
2019 2020
        return op_type not in self.OP_WITHOUT_KERNEL_SET

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    def to_string(self, throw_on_error):
2022
        """
2023 2024
        Get debug string.

2025
        Args:
2026 2027
            throw_on_error(bool): Whether to raise exception if self is not
                initialized.
2028

2029 2030
        Returns:
            str: The debug string.
2031 2032

        """
2033
        protostr = self.desc.serialize_to_string()
2034
        proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
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        return _debug_string_(proto, throw_on_error)

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    def _to_readable_code(self, skip_op_callstack=True):
        """
        Get readable debug string of Operator.

        .. note::
            If you want to get the debug string in protobuf format,
            please use :code:`to_string` method.

        Args:
            skip_op_callstack(bool): whether to skip parsing Operator's attribute
                op_callstack, default value is True

        Returns:
            string: The formatted Operator string.

        Examples:
            .. code-block:: python

            import paddle.fluid as fluid

            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            var = cur_block.create_var(name="X",
                                       shape=[-1, 23, 48],
                                       dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [var]},
                                outputs={"Out": [var]})
            print(new_op._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
        ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
            type(skip_op_callstack))
        outputs_str = "{"
        for i in range(0, len(self.output_names)):
            outputs_str += "{name}=".format(name=self.output_names[i])
            o = self.output(self.output_names[i])
            outputs_str += "{value}".format(value=o)
            if i != len(self.output_names) - 1:
                outputs_str += ", "
        outputs_str += "}"

        inputs_str = "{"
        for i in range(0, len(self.input_names)):
            inputs_str += "{name}=".format(name=self.input_names[i])
            o = self.input(self.input_names[i])
            inputs_str += "{value}".format(value=o)

            if i != len(self.input_names) - 1:
                inputs_str += ", "
        inputs_str += "}"

        attr_names = sorted(self.attr_names)
        attrs_str = ""
        for i in range(0, len(attr_names)):
            name = attr_names[i]
            if skip_op_callstack and name == "op_callstack":
                continue

            attr_type = self.desc.attr_type(name)
            if attr_type == core.AttrType.BLOCK:
                a = "{name} = block[{value}]".format(
                    name=name, type=attr_type, value=self._block_attr_id(name))
                attrs_str += a
                if i != len(attr_names) - 1:
                    attrs_str += ", "
                continue

            if attr_type == core.AttrType.BLOCKS:
                a = "{name} = blocks{value}".format(
                    name=name,
                    type=attr_type,
                    value=self._blocks_attr_ids(name))
                attrs_str += a
                if i != len(attr_names) - 1:
                    attrs_str += ", "
                continue

            a = "{name} = {value}".format(
                name=name, type=attr_type, value=self.desc.attr(name))
            attrs_str += a
            if i != len(attr_names) - 1:
                attrs_str += ", "

        if outputs_str != "{}":
            op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
                format(outputs = outputs_str, op_type=self.type, inputs=inputs_str, attrs=attrs_str)
        else:
            op_str = "{op_type}(inputs={inputs}, {attrs})".\
                format(op_type=self.type, inputs=inputs_str, attrs=attrs_str)
        return op_str

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    def __str__(self):
2131
        return self._to_readable_code()
2132 2133 2134

    __repr__ = __str__

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    @property
    def type(self):
2137
        return self.desc.type()
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    def input(self, name):
2140
        """
2141
        Get the input arguments according to the input parameter name.
2142

2143 2144
        Args:
            name(str): The input parameter name.
2145

2146 2147 2148
        Returns:
            list: return the list of argument names that associated with \
                the specific parameter name.
2149
        """
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        return self.desc.input(name)

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    def _rename_input(self, old_name, new_name):
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        """
        Rename the `old_name` to `new_name`.

        Args:
            old_name(str): The old name of the Operator's input.
            new_name(str): The new name of the Operator's input.

        Returns:
            None
        """
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        self.desc._rename_input(old_name, new_name)
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    def _rename_output(self, old_name, new_name):
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        """
        Rename the `old_name` to `new_name`.

        Args:
            old_name(str): The old name of the Operator's output.
            new_name(str): The new name of the Operator's output.

        Returns:
            None
        """
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        self.desc._rename_output(old_name, new_name)
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    @property
    def input_names(self):
        return self.desc.input_names()

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    @property
    def input_arg_names(self):
        return self.desc.input_arg_names()

    @property
    def output_arg_names(self):
        return self.desc.output_arg_names()

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    def output(self, name):
2191
        """
2192
        Get output arguments by the output parameter name.
2193

2194 2195
        Args:
            name(str): The output parameter name.
2196

2197 2198 2199
        Returns:
            list: return the list of argument names associated with \
                the specific parameter name.
2200
        """
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        return self.desc.output(name)

    @property
    def output_names(self):
        return self.desc.output_names()

2207 2208 2209 2210 2211 2212 2213 2214
    @property
    def idx(self):
        for i, op in enumerate(self.block.ops):
            if op == self:
                return i
        raise ValueError(
            "Can't find op itself in it's block. It could be a bug of Paddle.")

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    def has_attr(self, name):
2216
        """
2217 2218
        Whether this Operator has the attribute with name or not.

2219
        Args:
2220
            name(str): the attribute name.
2221

2222 2223
        Returns:
            bool: True if has this attribute.
2224 2225

        """
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        return self.desc.has_attr(name)

    def attr_type(self, name):
2229
        """
2230
        Get the type of attribute by attribute's name.
2231

2232 2233
        Args:
            name(str): the attribute name.
2234

2235 2236
        Returns:
            core.AttrType: the attribute type.
2237
        """
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        return self.desc.attr_type(name)

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    def _set_attr(self, name, val):
2241 2242 2243 2244 2245 2246 2247 2248 2249 2250
        """
        Set the value of attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.

        Raises:
            ValueError: If the type of value doesn't match with desc.attr_type(name).
        """
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        self._update_desc_attr(name, val)

2253 2254 2255
    def _remove_attr(self, name):
        self.desc.remove_attr(name)

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    def _update_desc_attr(self, name, val):
        """
        Update the value of desc's attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.

        Raises:
            ValueError: If the type of value doesn't match with desc.attr_type(name).
        """
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        if isinstance(val, Block):
            self.desc.set_block_attr(name, val.desc)
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        elif isinstance(val, list) and val and all(
                isinstance(v, Block) for v in val):
2271
            self.desc.set_blocks_attr(name, [v.desc for v in val])
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        elif isinstance(val, core.BlockDesc) or \
                isinstance(val, core.ProgramDesc):
            self.desc.set_serialized_attr(name, val.serialize_to_string())
        else:
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            self.desc._set_attr(name, val)
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    @property
    def attr_names(self):
        return self.desc.attr_names()

    def attr(self, name):
2283
        """
2284 2285
        Get the attribute by name.

2286
        Args:
2287
            name(str): the attribute name.
2288

2289 2290
        Returns:
            bool|int|str|float|list: The attribute value. The return value
2291 2292
            can be any valid attribute type.
        """
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        return self.desc.attr(name)
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    def _block_attr_id(self, name):
2296
        """
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        Get the block attribute's id by name.
2298

2299 2300
        Args:
            name(str): the attribute name.
2301

2302 2303
        Returns:
            int: the block index.
2304
        """
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        return self.desc._block_attr_id(name)
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    def _block_attr(self, name):
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        """
        Get the block attribute  by name.

        Args:
            name(str): the attribute name.

        Returns:
            block: the block attribute.
        """

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        id = self._block_attr_id(name)
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        assert (id >= 0 and id < len(self.block.program.blocks))
        return self.block.program.blocks[id]

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    def _blocks_attr(self, name):
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        """
        Get the blocks attribute  by name.

        Args:
            name(str): the attribute name.

        Returns:
            list: list of the blocks attribute.
        """
        attrs = []
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        for i in self._blocks_attr_ids(name):
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            assert (i >= 0 and i < len(self.block.program.blocks))
            attrs.append(self.block.program.blocks[i])

        return attrs

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    def _blocks_attr_ids(self, name):
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        """
        Get the blocks attribute's ids by name.

        Args:
            name(str): the attribute name.

        Returns:
            list: list of the blocks ids.
        """

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        return self.desc._blocks_attr_ids(name)
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    def all_attrs(self):
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        """
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        Get the attribute dict.

        Returns:
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            dict: The Operator's attribute dict, name->attr.
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        """
        attr_names = self.attr_names
        attr_map = {}
        for n in attr_names:
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            attr_type = self.desc.attr_type(n)
            if attr_type == core.AttrType.BLOCK:
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                attr_map[n] = self._block_attr(n)
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                continue

            if attr_type == core.AttrType.BLOCKS:
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                attr_map[n] = self._blocks_attr(n)
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                continue

            attr_map[n] = self.attr(n)

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        return attr_map

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    def _is_optimize_op(self):
        op_maker = core.op_proto_and_checker_maker
        OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize
        op_role = self.desc.attr(op_maker.kOpRoleAttrName())
        if op_role & int(OPTIMIZE):
            return True
        else:
            return False

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class Block(object):
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    """
    In Fluid, a Program is consistence of multi-Block, and Block stores
    VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
    One block could have some child blocks, and child block's name scopes
    should inherit the parent's so that OpDesc in child block can reference
    a VarDesc that is stored in the parent block.
    Please reference the framework.proto for details.

    Args:
        program(Program): The Program that the Block belongs to.
        idx(int): The block's id in the Program.

    Notes:
        The constructor of Block should not be invoked directly. Please
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        use `Program._create_block()` to create a block.
2401 2402 2403 2404

    Examples:
        .. code-block:: python

2405 2406 2407
            import paddle.fluid as fluid

            cur_program = fluid.Program()
2408 2409 2410 2411 2412 2413 2414 2415 2416
            cur_block = cur_program.current_block()
            var = cur_block.create_var(name="X",
                                       shape=[-1, 23, 48],
                                       dtype='float32')
            cur_block.append_op(type="abs",
                                inputs={"X": [var]},
                                outputs={"Out": [var]})
    """

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    def __init__(self, program, idx):
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        self.desc = program.desc.block(idx)
2419
        self.vars = collections.OrderedDict()  # var_name --> var
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        self.ops = list()  # operator list
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        self.program = program
2422
        self.removed_vars = collections.OrderedDict()
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2424
    def __str__(self):
2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470
        return self._to_readable_code()

    def _to_readable_code(self, skip_op_callstack=True):
        """
        Get readable debug string of Block.

        .. note::
            If you want to get the debug string in protobuf format,
            please use :code:`to_string` method.

        Args:
            skip_op_callstack(bool): whether to skip parsing Operator's attribute
                op_callstack, default value is True

        Returns:
            string: The formatted Block string.

        Examples:
            .. code-block:: python

            import paddle.fluid as fluid

            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_var = cur_block.create_var(name="X",
                                           shape=[-1, 23, 48],
                                           dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [new_var]},
                                outputs={"Out": [new_var]})
            print(cur_block._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
        ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
            type(skip_op_callstack))
        block_str = "{ // block "
        block_str += "{}\n".format(self.idx)
        for var in list(self.vars.values()):
            block_str += "    {}\n".format(var._to_readable_code())
        block_str += "\n"
        for op in self.ops:
            block_str += "    {}\n".format(
                op._to_readable_code(skip_op_callstack))
        block_str += "}"
        return block_str
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    def to_string(self, throw_on_error, with_details=False):
        """
2474 2475
        Get debug string.

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        Args:
            throw_on_error(bool): raise exception when self is not initialized
2478
                when throw_on_error is True.
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            with_details(bool): more details about variables and parameters
2480 2481
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False.
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2483 2484
        Returns:
            str: The debug string.
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        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        if with_details:
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            re_add_indent = re.compile(r"\n(.)")
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            res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
                self.idx, self.parent_idx)
2492
            for var in list(self.vars.values()):
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                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
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                    r"\n    \1", var.to_string(throw_on_error, with_details))
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            for op in self.ops:
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                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                    r"\n    \1", op.to_string(throw_on_error))
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            res_str += "\n}"
        else:
            protostr = self.desc.serialize_to_string()
2501 2502
            proto = framework_pb2.BlockDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
2505 2506 2507

    __repr__ = __str__

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    @property
    def parent_idx(self):
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        return self.desc.parent
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    @property
    def forward_block_idx(self):
        return self.desc.get_forward_block_idx()

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    def _set_forward_block_idx(self, idx):
2517 2518 2519 2520 2521 2522 2523 2524 2525
        """
        Set the forward block Idx.

        Args:
            idx(int): the block index.

        Returns:
            None
        """
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        self.desc._set_forward_block_idx(idx)
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2528 2529 2530 2531 2532 2533 2534 2535
    @property
    def backward_block_idx(self):
        cur_block_idx = self.idx
        for block in self.program.blocks:
            if block.forward_block_idx == cur_block_idx:
                return block.idx
        return -1

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    @property
    def idx(self):
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        return self.desc.id
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    def var(self, name):
2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553
        """
        Get a Variable by name from this block.

        Args:
            name(str): the Variable's name.

        Raises:
            ValueError: The If input's type is not str, or this block
                doesn't have a Variable with the giving name.

        Returns:
            Variable: the Variable with the giving name.
        """
2554
        if not isinstance(name, six.string_types):
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            raise TypeError(
                "var require string as parameter, but get %s instead." %
                (type(name)))
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        v = self.vars.get(name, None)
        if v is None:
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            raise ValueError("var %s not in this block" % name)
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        return v
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    def _find_var_recursive(self, name):
2564 2565 2566 2567 2568 2569 2570
        """
        Get a Variable by name from this block recursively.

        Args:
            name(str): the Variable's name.

        Returns:
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            Variable: the Variable with the giving name. Or None if not found.
2572
        """
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        frontier = list()
        visited = set()

        frontier.append(self)

        prog = self.program

        while len(frontier) != 0:  # BFS
            cur = frontier[0]
            frontier = frontier[1:]

            if id(cur) in visited:
                continue

            if cur.has_var(name):
                return cur.var(name)

            if cur.parent_idx != -1:
                frontier.append(prog.block(cur.parent_idx))

            if cur.forward_block_idx != -1:
                frontier.append(prog.block(cur.forward_block_idx))

            visited.add(id(cur))
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        return None
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    def _var_recursive(self, name):
        """
        Get a Variable by name from this block recursively.

        Args:
            name(str): the Variable's name.

        Raises:
            ValueError: this block and this parent block doesn't
                have a Variable with the giving name.

        Returns:
            Variable: the Variable with the giving name.
        """
        var = self._find_var_recursive(name)
        if var:
            return var
        else:
            raise ValueError("Var {0} is not found recursively".format(name))
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    def all_parameters(self):
2620
        return list(self.iter_parameters())
2621

2622
    def iter_parameters(self):
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        return (item[1] for item in six.iteritems(self.vars)
2624
                if isinstance(item[1], Parameter))
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    def create_var(self, *args, **kwargs):
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        if in_dygraph_mode():
            var = _varbase_creator(*args, **kwargs)
        else:
2630 2631 2632
            var = Variable(block=self, *args, **kwargs)
            if 'initializer' in kwargs:
                kwargs['initializer'](var, self)
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        return var
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    def has_var(self, name):
        return name in self.vars

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    def _rename_var(self, name, new_name):
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        """
        Rename variable in vars and ops' inputs and outputs
2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652

        Args:
            name(str): the name that need to be renamed.
            new_name(str): the name that need to rename to.

        Raises:
            ValueError: If this block doesn't have this the giving name,
                or the type of the var with the giving name is not Parameter
                or Variable.

        Returns:
            Variable: the Variable with the giving name.
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        """
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        name = cpt.to_text(name)
        new_name = cpt.to_text(new_name)
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        if not self.has_var(name):
2658
            raise ValueError("var %s is not in current block" % name)
T
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2659 2660
        v = self.var(name)
        if type(v) == Parameter:
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            var_type = "Parameter"
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            stop_gradient = v.stop_gradient
            trainable = v.trainable
            optimize_attr = v.optimize_attr
            regularizer = v.regularizer
            error_clip = v.error_clip
        elif type(v) == Variable:
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            var_type = "Variable"
T
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            error_clip = v.error_clip
            stop_gradient = v.stop_gradient
        else:
            raise ValueError("unsupported var type: %s", type(v))
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        orig_var_type = v.type
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        self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
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        # NOTE: v is destroyed by C++ after calling _rename_var.
M
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        d = self.desc.find_var(cpt.to_bytes(new_name))
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        if var_type == "Parameter":
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2678 2679
            if in_dygraph_mode():
                var = ParamBase(
2680 2681 2682 2683 2684 2685 2686 2687 2688 2689
                    d.shape(),
                    d.dtype(),
                    type=orig_var_type,
                    name=new_name,
                    stop_gradient=stop_gradient,
                    trainable=trainable,
                    optimize_attr=optimize_attr,
                    regularizer=regularizer,
                    error_clip=error_clip)
            else:
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2690 2691
                var = Parameter(
                    self,
2692 2693 2694 2695 2696 2697 2698 2699 2700
                    d.shape(),
                    d.dtype(),
                    type=orig_var_type,
                    name=new_name,
                    stop_gradient=stop_gradient,
                    trainable=trainable,
                    optimize_attr=optimize_attr,
                    regularizer=regularizer,
                    error_clip=error_clip)
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        elif var_type == "Variable":
T
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2702 2703
            var = Variable(
                self,
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2704
                type=orig_var_type,
T
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2705 2706 2707 2708
                name=new_name,
                error_clip=error_clip,
                stop_gradient=stop_gradient)

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2709
        # rename the python side, _sync_with_cpp will only add
T
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2710 2711 2712
        # new vars/ops to python side.
        self.vars[new_name] = var
        del self.vars[name]
W
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        self._sync_with_cpp()
2714
        return var
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W
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2716 2717
    def _remove_var(self, name):
        self._sync_with_cpp()
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2718
        self.desc._remove_var(cpt.to_bytes(name))
2719 2720
        del self.vars[name]

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2721 2722
    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
2723
        param = None
L
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2724
        if in_dygraph_mode():
2725
            param = ParamBase(*args, **kwargs)
L
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2726 2727
        else:
            param = Parameter(global_block, *args, **kwargs)
2728
        if 'initializer' in kwargs:
2729 2730 2731 2732 2733

            def _is_inited_by(block, var):
                init_ops = []
                for op in block.ops:
                    if var.name in op.output_arg_names:
2734 2735 2736 2737 2738
                        # In startup_program, "c_broadcast" and "c_sync_comm_stream"
                        # are treated as initialization ops that cause error. 
                        # Think of "c_broadcast" and "c_sync_comm_stream" as a special case here.
                        if op.type in ["c_broadcast", "c_sync_comm_stream"]:
                            continue
2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749
                        init_ops.append(op)
                return init_ops

            initializer = kwargs['initializer']
            init_ops = _is_inited_by(global_block, param)
            init_ops_len = len(init_ops)
            if init_ops_len > 1:
                raise RuntimeError("param " + param.name +
                                   " is inited by multiple init ops " + str(
                                       init_ops))
            elif init_ops_len == 1:
2750
                # TODO already inited, do nothing, should log a warning
2751 2752 2753
                pass
            else:
                initializer(param, self)
2754
        param.stop_gradient = False
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2755
        return param
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    def append_op(self, *args, **kwargs):
2758 2759 2760 2761 2762 2763
        """
        Appends a new Operator according to the giving arguments.

        Returns:
            Operator: the append Operator.
        """
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        if in_dygraph_mode():
2765
            attrs = kwargs.get("attrs", {})
J
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            type = kwargs.get("type", None)
2767 2768 2769
            op = Operator(
                block=self,
                desc=None,
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2770
                type=type,
M
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2771 2772
                inputs=None,
                outputs=None,
2773
                attrs=attrs)
2774

M
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2775 2776 2777
            # record ops in tracer rather than blocks
            #
            # TODO(minqiyang): add op stop_gradient support in static mode too.
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            # currently, we only support stop_gradient in dygraph mode.
J
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2779 2780

            _dygraph_tracer().trace_op(type,
M
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                                       kwargs.get("inputs", {}),
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2782 2783
                                       kwargs.get("outputs", {}), attrs
                                       if attrs else {},
M
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                                       kwargs.get("stop_gradient", False))
M
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2785
        else:
2786 2787 2788 2789 2790 2791 2792 2793 2794
            op_desc = self.desc.append_op()
            op = Operator(
                block=self,
                desc=op_desc,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))

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            self.ops.append(op)
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2797 2798
        return op

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    def _insert_op(self, index, *args, **kwargs):
2800 2801 2802 2803 2804 2805 2806 2807 2808
        """
        Insert a Operator according to the giving arguments.

        Args:
            index(int): the place that the operator to insert.

        Returns:
            Operator: the insert Operator.
        """
W
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2809 2810
        self._sync_with_cpp()
        op_desc = self.desc._insert_op(index)
Q
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2811 2812 2813 2814
        op = Operator(block=self, desc=op_desc, *args, **kwargs)
        self.ops.insert(index, op)
        return op

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2815
    def _remove_op(self, index):
2816 2817 2818 2819 2820 2821 2822 2823 2824
        """
        Remove the specific position operator.

        Args:
            index(int): the position that the operator to insert.

        Returns:
            None
        """
W
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2825 2826
        self._sync_with_cpp()
        self.desc._remove_op(index, index + 1)
2827 2828
        del self.ops[index]

W
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2829
    def _slice_ops(self, start, end):
2830 2831 2832 2833 2834 2835 2836 2837 2838 2839
        """
        Return the Operator between start and end.

        Args:
            start(int): the start position.
            end(int): the end position.

        Returns:
            list: the Operators between start and end.
        """
Q
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2840
        return self.ops[start:end]
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2841

W
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2842
    def _prepend_op(self, *args, **kwargs):
L
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2843
        if in_dygraph_mode():
J
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2844 2845
            type = kwargs.get("type", None)
            attrs = kwargs.get("attrs", {})
2846
            op = Operator(
J
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2847
                self, None, type=type, inputs=None, outputs=None, attrs=attrs)
M
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2848

J
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2849
            _dygraph_tracer().trace_op(type,
M
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2850
                                       kwargs.get("inputs", {}),
J
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2851 2852
                                       kwargs.get("outputs", {}), attrs
                                       if attrs else {},
M
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2853
                                       kwargs.get("stop_gradient", False))
M
minqiyang 已提交
2854
        else:
2855 2856 2857 2858 2859 2860 2861 2862
            op_desc = self.desc._prepend_op()
            op = Operator(
                self,
                op_desc,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))
M
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2863
            self.ops.insert(0, op)
2864

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2865 2866
        return op

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2867
    def _sync_with_cpp(self):
2868
        """
2869 2870
        Sync from the desc on the c++ end. This method is used to synchronize
        the c++ desc instance generated by backward.
2871
        """
Q
Qiao Longfei 已提交
2872 2873 2874 2875 2876
        # sync variables from cpp
        for var in self.desc.all_vars():
            if not self.has_var(var.name()):
                self.create_var(name=var.name(), desc=var, type=var.type())

2877
        # sync variables removed from c++ end
2878
        for var in list(self.vars.keys()):
M
minqiyang 已提交
2879
            if not self.desc.find_var(cpt.to_bytes(var)):
2880 2881
                self.vars.pop(var)

Q
Qiao Longfei 已提交
2882
        # sync operators from cpp
2883 2884 2885 2886
        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

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2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902
        if len(self.ops) != 0:
            first_op_in_python = self.ops[0].desc
            last_op_in_python = self.ops[len(self.ops) - 1].desc
            start_index = None
            end_index = None
            for index in range(len(ops_in_cpp)):
                if first_op_in_python == ops_in_cpp[index]:
                    start_index = index
                if last_op_in_python == ops_in_cpp[index]:
                    end_index = index
            assert start_index is not None
            assert end_index is not None
            assert start_index <= end_index
        else:
            start_index = 0
            end_index = -1
Q
Qiao Longfei 已提交
2903 2904 2905 2906 2907

        # sync ops append to the head of cpp_ops
        for index in range((start_index - 1 - 1), -1, -1):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
Q
qiaolongfei 已提交
2908
            self.ops.insert(0, op)
Q
Qiao Longfei 已提交
2909 2910 2911 2912 2913 2914 2915

        # sync ops append to the end of cpp_ops
        for index in range((end_index + 1), len(ops_in_cpp)):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
            self.ops.append(op)

2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928
        # sync ops removed from c++ end
        if end_index != -1 and end_index < len(self.ops):
            ops_in_cpp_index = 0
            ops_in_python_index = 0
            while ops_in_python_index < len(
                    self.ops) and ops_in_cpp_index < len(ops_in_cpp):
                if self.ops[ops_in_python_index].desc != ops_in_cpp[
                        ops_in_cpp_index]:
                    del self.ops[ops_in_python_index]
                else:
                    ops_in_cpp_index += 1
                    ops_in_python_index += 1

Q
Qiao Longfei 已提交
2929 2930 2931 2932
        assert len(self.ops) == len(ops_in_cpp)
        for index in range(len(self.ops)):
            assert self.ops[index].desc == ops_in_cpp[index]

W
Wu Yi 已提交
2933
    def _copy_param_info_from(self, other):
2934
        """
2935 2936
        Copy the information of parameters from the other block.

2937
        Args:
2938 2939 2940 2941 2942
            other(Block): the other block.

        Raises:
            ValueError: If type of input is not Block, or the `other` and this
                block is not in the same topology.
2943 2944 2945 2946 2947

        Returns:
            None
        """
        if not isinstance(other, Block):
W
Wu Yi 已提交
2948 2949
            raise TypeError(
                "_copy_param_info_from should be invoked with Block")
2950
        for p in other.iter_parameters():
2951 2952 2953
            assert isinstance(p, Parameter)
            v = self.vars.get(p.name, None)
            if v is None:
2954 2955
                # if the Parameter is pruned, v may be None
                continue
2956
            assert isinstance(v, Variable)
2957
            new_p = None
L
Leo Chen 已提交
2958 2959
            if in_dygraph_mode():
                new_p = ParamBase(
2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970
                    shape=v.shape,
                    dtype=v.dtype,
                    type=v.type,
                    lod_level=v.lod_level,
                    stop_gradient=p.stop_gradient,
                    trainable=p.trainable,
                    optimize_attr=p.optimize_attr,
                    regularizer=p.regularizer,
                    error_clip=p.error_clip,
                    name=v.name)
            else:
L
Leo Chen 已提交
2971 2972
                new_p = Parameter(
                    block=self,
2973 2974 2975 2976 2977 2978 2979 2980 2981 2982
                    shape=v.shape,
                    dtype=v.dtype,
                    type=v.type,
                    lod_level=v.lod_level,
                    stop_gradient=p.stop_gradient,
                    trainable=p.trainable,
                    optimize_attr=p.optimize_attr,
                    regularizer=p.regularizer,
                    error_clip=p.error_clip,
                    name=v.name)
2983 2984
            self.vars[new_p.name] = new_p

2985
    def _clone_variable(self, var, force_persistable=True):
2986 2987
        """
        Clone a variable into current block.
2988

2989 2990
        Args:
            var: the variable to be cloned.
2991 2992 2993
            force_persistable(bool): True means setting the result variable to being persistable.
                                     False means setting the persistable the same with that of input var.
                                     default: True.
2994 2995

        Returns:
2996
            Variable: the new  variable cloned from 'var' in current block.
2997 2998
        """
        assert isinstance(var, Variable)
T
update  
typhoonzero 已提交
2999 3000 3001 3002 3003
        ret_var = None
        # make STEP_SCOPES var can be safely cloned.
        if var.type == core.VarDesc.VarType.STEP_SCOPES:
            ret_var = self.create_var(
                name=var.name, persistable=var.persistable, type=var.type)
T
tangwei12 已提交
3004 3005
        elif var.type == core.VarDesc.VarType.RAW:
            ret_var = self.create_var(
T
tangwei12 已提交
3006
                name=var.name, persistable=var.persistable, type=var.type)
T
typhoonzero 已提交
3007 3008 3009 3010 3011 3012
        elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
3013
                persistable=True if force_persistable else var.persistable,
H
Huihuang Zheng 已提交
3014 3015
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
typhoonzero 已提交
3016 3017 3018 3019 3020 3021 3022
        else:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
3023
                persistable=True if force_persistable else var.persistable,
H
Huihuang Zheng 已提交
3024 3025
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
typhoonzero 已提交
3026
        return ret_var
3027

Y
Yu Yang 已提交
3028

3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123
class IrNode(object):
    """
    Python IrNode. Beneath it is a core.Node, which is used for Ir Pass.
    """

    def __init__(self, node):
        """
        Construct an IrNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node,
                          core.Node), 'node must be the instance of core.Node.'
        self.node = node

    def name(self):
        """
        Return the node name.

        Returns:
            str: node name.
        """
        return self.node.name()

    def node_type(self):
        """
        Return the node type.

        Returns:
            core.Node.Type: node type(core.Node.Type.Operation or core.Node.Type.Variable).
        """
        return self.node.node_type()

    def var(self):
        """
        Return the node variable description.

        Returns:
            core.VarDesc: node variable description.
        """
        return self.node.var()

    def op(self):
        """
        Return the node operator description.

        Returns:
            core.OpDesc: node operator description.
        """
        return self.node.op()

    def id(self):
        """
        Return the node id.

        Returns:
            int: node id.
        """
        return self.node.id()

    def is_op(self):
        """
        If the node is an operator, then return true.

        Returns:
            bool: indicate whether the node is an operator.
        """
        return self.node.is_op()

    def is_var(self):
        """
        If the node is a variable, then return true.

        Returns:
            bool: indicate whether the node is a variable.
        """
        return self.node.is_var()

    def is_ctrl_var(self):
        """
        If the node is a control dependence variable, then return true.

        Returns:
            bool: indicate whether the node is a control dependence variable.
        """
        return self.node.is_ctrl_var()

    def clear_inputs(self):
        """
        Clear the node inputs. After executing the `clear_inputs` function,
        the node inputs will be empty.
        """
        self.node.clear_inputs()

3124
    def remove_input_by_id(self, node_id):
3125 3126 3127 3128 3129 3130
        """
        Remove a node from inputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
3131
        self.node.remove_input(node_id)
3132

3133
    def remove_input(self, node):
3134 3135 3136 3137
        """
        Remove a node from inputs.

        Args:
3138
            node(IrNode): the node being removed.
3139
        """
3140
        self.node.remove_input(node.node)
3141

3142
    def append_input(self, node):
3143 3144 3145 3146
        """
        Append a node in inputs.

        Args:
3147
            node(IrNode): the node being appended.
3148
        """
3149
        self.node.append_input(node.node)
3150 3151 3152 3153 3154 3155 3156 3157

    def clear_outputs(self):
        """
        Clear the node outputs. After executing the `clear_outputs` function,
        the node outputs will be empty.
        """
        self.node.clear_outputs()

3158
    def remove_output_by_id(self, node_id):
3159 3160 3161 3162 3163 3164
        """
        Remove a node from outputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
3165
        self.node.remove_output(node_id)
3166

3167
    def remove_output(self, node):
3168 3169 3170 3171
        """
        Remove a node from outputs.

        Args:
3172
            node(IrNode): the node being removed.
3173
        """
3174
        self.node.remove_output(node.node)
3175

3176
    def append_output(self, node):
3177 3178 3179 3180
        """
        Append a node in outputs.

        Args:
3181
            node(IrNode): the node being appended.
3182
        """
3183
        self.node.append_output(node.node)
3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230

    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrNode): node inputs wrapped by IrNode.
        """
        return [IrNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrNode): node outputs wrapped by IrNode.
        """
        return [IrNode(n) for n in self.node.outputs]


class IrVarNode(IrNode):
    """
    Python IrVarNode. Beneath it is a core.Node, it inherits from IrNode.
    """

    def __init__(self, node):
        """
        Construct an IrVarNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node, core.Node) and node.is_var(), \
            'node must be the instance of core.Node and it must be a variable node.'
        super(IrVarNode, self).__init__(node)
        self.node = node

    def set_shape(self, shape):
        """
        Set the node variable shape.

        Args:
            shape(list): shape to be set.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
3231
            "The node variable description can not be None."
3232 3233 3234 3235 3236 3237 3238 3239 3240 3241
        self.node.var().set_shape(shape)

    def persistable(self):
        """
        If the variable node is a persistable variable, then return true.

        Returns:
            bool: indicate whether the variable is persistable.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
3242
            "The node variable description can not be None."
3243 3244
        return self.node.var().persistable()

3245 3246 3247 3248 3249 3250 3251 3252
    def type(self):
        """
        Return the variable type.

        Returns:
            core.VarDesc.VarType: the variable type.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
3253
            "The node variable description can not be None."
3254 3255 3256 3257 3258 3259 3260 3261 3262 3263
        return self.node.var().type()

    def dtype(self):
        """
        Return the variable data type.

        Returns:
            core.VarDesc.VarType: the variable data type.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
3264
            "The node variable description can not be None."
3265 3266 3267 3268 3269 3270 3271 3272 3273 3274
        return self.node.var().dtype()

    def shape(self):
        """
        Return the variable shape.

        Returns:
            list: the variable shape.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
3275
            "The node variable description can not be None."
3276 3277
        return self.node.var().shape()

3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324
    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrOpNode): node inputs wrapped by IrOpNode.
        """
        return [IrOpNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrOpNode): node outputs wrapped by IrOpNode.
        """
        return [IrOpNode(n) for n in self.node.outputs]


class IrOpNode(IrNode):
    """
    Python IrOpNode. Beneath it is a core.Node, it inherits from IrNode.
    """

    def __init__(self, node):
        """
        Construct an IrOpNode using core.Node.

        Args:
            node(core.Node): C++ Node.
        """
        assert isinstance(node, core.Node) and node.is_op(), \
            'node must be the instance of core.Node and it must be a operator node.'
        super(IrOpNode, self).__init__(node)
        self.node = node

    def rename_input(self, old_input_name, new_input_name):
        """
        Rename the input of this node.

        Args:
            old_input_name(str): the old input name.
            new_input_name(str): the new input name.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3325
            "The node operator description can not be None."
3326 3327
        self.node.op()._rename_input(old_input_name, new_input_name)

3328 3329 3330 3331 3332 3333 3334 3335 3336
    def rename_output(self, old_output_name, new_output_name):
        """
        Rename the output of this node.

        Args:
            old_output_name(str): the old output name.
            new_output_name(str): the new output name.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3337
            "The node operator description can not be None."
3338 3339 3340 3341
        print("op: {}, old: {}, new: {}\n".format(self.node.op().type(
        ), old_output_name, new_output_name))
        self.node.op()._rename_output(old_output_name, new_output_name)

3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352
    def input(self, name):
        """
        Get the argument name list by the parameter name for input.

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3353
            "The node operator description can not be None."
3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366
        return self.node.op().input(name)

    def output(self, name):
        """
        Get the argument name list by the parameter name for output.

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3367
            "The node operator description can not be None."
3368 3369 3370 3371 3372 3373 3374 3375 3376 3377
        return self.node.op().output(name)

    def set_type(self, new_type):
        """
        Change the operator type into new type.

        Args:
            new_type(str): new operator type to be set.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3378
            "The node operator description can not be None."
3379 3380
        return self.node.op().set_type(new_type)

3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395
    def set_attr(self, name, val):
        """
        Set the value of attribute by attribute's name.

        Args:
            name(str): the attribute name.
            val(bool|int|str|float|list): the value of the attribute.
        """
        self._update_desc_attr(name, val)

    def _update_desc_attr(self, name, val):
        """
        Update the value of the op desc's attribute by attribute's name.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3396
            "The node operator description can not be None."
3397 3398 3399 3400
        desc = self.node.op()
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and \
3401
                all(isinstance(v, Block) for v in val):
3402 3403
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
3404
                isinstance(val, core.ProgramDesc):
3405 3406 3407 3408
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)

3409 3410 3411 3412 3413 3414 3415 3416
    def input_arg_names(self):
        """
        Return input arguments' names of this op node.

        Returns:
            list(str): input arguments' names of this op node.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3417
            "The node operator description can not be None."
3418 3419 3420 3421 3422 3423 3424 3425 3426 3427
        return self.node.op().input_arg_names()

    def output_arg_names(self):
        """
        Return output arguments' names of this op node.

        Returns:
            list(str): output arguments' names of this op node.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
3428
            "The node operator description can not be None."
3429 3430
        return self.node.op().output_arg_names()

3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451
    @property
    def inputs(self):
        """
        Return the node inputs.

        Returns:
            list(IrVarNode): node inputs wrapped by IrVarNode.
        """
        return [IrVarNode(n) for n in self.node.inputs]

    @property
    def outputs(self):
        """
        Return the node outputs.

        Returns:
            list(IrVarNode): node outputs wrapped by IrVarNode.
        """
        return [IrVarNode(n) for n in self.node.outputs]


3452 3453
class IrGraph(object):
    """
3454
    Python IrGraph. Beneath it is a core.Graph, which is used for
3455
    creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
3456 3457
    a Program. In an IrGraph, both Variables and Operators are graph
    nodes.
3458 3459 3460 3461
    """

    def __init__(self, graph, for_test=False):
        """
3462 3463
        Construct an IrGraph using core.Graph.

3464 3465 3466 3467 3468 3469 3470 3471 3472
        Args:
            graph(core.Graph): C++ Graph.
            for_test(bool): True for the test graph and false for the train graph.
        """
        assert isinstance(
            graph, core.Graph), 'graph must be the instance of core.Graph.'
        self.graph = graph
        self._for_test = for_test

3473 3474 3475 3476
    def clone(self):
        """
        Create a new and duplicated IrGraph.

3477 3478 3479
        Warns:
            The method only clones the graph structure, not its attributes.

3480 3481 3482
        Returns:
            IrGraph: A new and duplicated graph.
        """
3483
        g = self.graph.clone()
3484 3485
        return IrGraph(g, self._for_test)

3486
    def is_test(self):
3487 3488 3489
        """
        If the graph is used for testing, the function returns true. Otherwise, returns false.
        """
3490 3491
        return self._for_test

W
WangZhen 已提交
3492
    def all_nodes(self):
3493 3494 3495
        """
        Return all nodes included in the graph as a set.
        """
3496
        return {IrNode(node) for node in self.graph.nodes()}
3497

3498
    def all_var_nodes(self):
3499 3500 3501
        """
        Return all variable nodes included in the graph as a set.
        """
3502
        return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
3503

3504
    def all_persistable_nodes(self):
3505 3506 3507
        """
        Return all persistable variable nodes included in the graph as a set.
        """
W
WangZhen 已提交
3508 3509 3510 3511 3512
        persistable_nodes = set()
        for node in self.graph.nodes():
            if node.is_var() and node.var() is not None and node.var(
            ).persistable():
                persistable_nodes.add(node)
3513
        return {IrVarNode(p) for p in persistable_nodes}
W
WangZhen 已提交
3514

3515
    def all_op_nodes(self):
3516 3517 3518
        """
        Return all operator nodes included in the graph as a set.
        """
3519
        return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
3520

3521
    def create_persistable_node(self, name, var_type, shape, var_dtype):
3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532
        """
        Create a persistable variable node in the graph. In IrGraph,
        it can not distinguish between persistable variables and parameters.

        Args:
            name(str): the name of the persistable variable node.
            vart_type(core.VarDesc.VarType): the type of the persistable variable node.
            shape(list): the shape of the persistable variable node.
            var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.

        Returns:
3533
            IrVarNode: the created persistable variable node.
3534
        """
3535 3536 3537 3538 3539
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
        var_desc.set_persistable(True)
3540
        return IrVarNode(self.graph.create_var_node(var_desc))
3541 3542

    def create_var_node(self, name, var_type, shape, var_dtype):
3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553
        """
        Create a variable node in the graph. The created variable node is
        not persistable.

        Args:
            name(str): the name of the variable node.
            vart_type(core.VarDesc.VarType): the type of the variable node.
            shape(list): the shape of the variable node.
            var_dtype(core.VarDesc.VarType): the data type of the variable node.

        Returns:
3554
            IrVarNode: the created variable node.
3555 3556
        """

3557 3558 3559 3560
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
3561
        return IrVarNode(self.graph.create_var_node(var_desc))
3562 3563

    def create_var_node_from_desc(self, var_desc):
3564 3565 3566 3567 3568 3569 3570 3571
        """
        Create a variable node by using an existing VarDesc in the graph.
        Depend on the giving VarDesc, the created variable node may be persistable.

        Args:
            var_desc(core.VarDesc): the giving variable description.

        Returns:
3572
            IrVarNode: the created variable node.
3573
        """
3574
        return IrVarNode(self.graph.create_var_node(var_desc))
3575 3576

    def create_op_node(self, op_type, attrs, inputs, outputs):
3577 3578 3579 3580 3581 3582 3583
        """
        Create a operator node in the graph.

        Args:
            op_type(str): the type of the operator node.
            attrs(dict): the attributes of the operator node.
            inputs(dict): the inputs of the operator node.
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tianshuo78520a 已提交
3584
            outputs(dict): the outputs of the operator node.
3585 3586

        Returns:
3587
            IrOpNode: the created operator node.
3588
        """
3589 3590
        op_desc = core.OpDesc()
        op_desc.set_type(op_type)
3591
        for attr, value in six.iteritems(attrs):
3592
            self._update_desc_attr(op_desc, attr, value)
3593
        for input_name, var_nodes in six.iteritems(inputs):
3594 3595 3596 3597
            if not isinstance(var_nodes, list):
                var_nodes = [var_nodes]
            op_desc.set_input(input_name,
                              [var_node.name() for var_node in var_nodes])
3598
        for output_name, var_nodes in six.iteritems(outputs):
3599 3600 3601 3602
            if not isinstance(var_nodes, list):
                var_nodes = [var_nodes]
            op_desc.set_output(output_name,
                               [var_node.name() for var_node in var_nodes])
3603
        return IrOpNode(self.graph.create_op_node(op_desc))
3604 3605

    def create_op_node_from_desc(self, op_desc):
3606 3607 3608 3609 3610 3611 3612
        """
        Create a operator node by using an existing OpDesc in the graph.

        Args:
            op_desc(core.VarDesc): the giving operator description.

        Returns:
3613
            IrOpNode: the created operator node.
3614
        """
3615
        return IrOpNode(self.graph.create_op_node(op_desc))
3616 3617

    def update_input_link(self, old_input_node, new_input_node, op_node):
3618 3619 3620 3621
        """
        Update the input's link of a operator node.

        Args:
3622 3623 3624
            old_input_node(IrNode): the old input node of the giving op_node.
            new_input_node(IrNode): the new input node of the giving op_node.
            op_node(IrOpNode): the operator node that is needed to update input's link.
3625
        """
3626
        assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
3627 3628
               self.graph.nodes() and op_node.node in self.graph.nodes(), \
            'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
3629 3630 3631 3632
        old_input_node.remove_output(op_node)
        op_node.remove_input(old_input_node)
        new_input_node.append_output(op_node)
        op_node.append_input(new_input_node)
3633
        op_node.rename_input(old_input_node.name(), new_input_node.name())
3634

3635 3636 3637 3638 3639 3640 3641 3642 3643 3644
    def update_output_link(self, old_output_node, new_output_node, op_node):
        """
        Update the output's link of an operator node.

        Args:
            old_output_node(IrNode): the old output node of the giving op_node.
            new_output_node(IrNode): the new output node of the giving op_node.
            op_node(IrOpNode): the operator node that is needed to update input's link.
        """
        assert old_output_node.node in self.graph.nodes() and new_output_node.node in \
3645 3646
               self.graph.nodes() and op_node.node in self.graph.nodes(), \
            'The three arguments(old_output_node &new_output_node &op_node) must be in the graph nodes.'
3647 3648 3649 3650 3651 3652
        old_output_node.remove_input(op_node)
        op_node.remove_output(old_output_node)
        new_output_node.append_input(op_node)
        op_node.append_output(new_output_node)
        op_node.rename_output(old_output_node.name(), new_output_node.name())

3653
    def link_to(self, node_in, node_out):
3654 3655 3656 3657
        """
        Connect two nodes.

        Args:
3658 3659
            node_in(IrNode): the input node.
            node_out(IrNode): the output node.
3660
        """
3661
        assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \
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3662
            'The two arguments(node_in&node_out) must be in the graph nodes.'
3663 3664
        node_in.append_output(node_out)
        node_out.append_input(node_in)
3665 3666

    def safe_remove_nodes(self, remove_nodes):
3667 3668 3669 3670 3671 3672 3673
        """
        Remove nodes safely since links connected to these removed nodes are
        also removed.

        Args:
            remove_nodes(set): the nodes prepared to be removed.
        """
3674
        if not isinstance(remove_nodes, set):
W
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3675 3676 3677 3678
            if isinstance(remove_nodes, Iterable):
                remove_nodes = set(remove_nodes)
            else:
                remove_nodes = {remove_nodes}
3679 3680
        original_nodes = {n.node for n in remove_nodes}
        core.graph_safe_remove_nodes(self.graph, original_nodes)
3681

Z
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3682 3683 3684 3685 3686 3687 3688 3689
    def resolve_hazard(self):
        ordered_nodes = core.topology_sort(self.graph)
        var_nodes = dict()
        for node in ordered_nodes:
            if node.is_op() and node.op() is not None:
                for each_var_name in node.op().input_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
3690
                            self._find_node_by_name(node.inputs, each_var_name)
Z
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3691 3692 3693 3694
                        ]
                for each_var_name in node.op().output_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
3695
                            self._find_node_by_name(node.outputs, each_var_name)
Z
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3696 3697 3698
                        ]
                    else:
                        var_nodes[each_var_name].append(
3699 3700
                            self._find_node_by_name(node.outputs,
                                                    each_var_name))
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3701 3702
        self.graph.resolve_hazard(var_nodes)

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    def has_circle(self):
3704 3705 3706 3707 3708 3709
        """
        Check if the graph has a circle.

        Returns:
            bool: True if the graph has a circle else False.
        """
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3710 3711 3712
        return core.has_circle(self.graph)

    def graph_num(self):
3713 3714 3715 3716 3717 3718
        """
        Count the number of unconnected graphs in this graph.

        Returns:
            int: the number of unconnected graphs.
        """
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3719 3720 3721
        return core.graph_num(self.graph)

    def topology_sort(self):
3722 3723 3724
        """
        Perform the topology sort operation on the graph.

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3725
        Notes: the `graph` can not contain a circle.
3726 3727

        Returns:
Z
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3728
            list(IrNode): nodes in topology order.
3729
        """
3730
        ordered_nodes = core.topology_sort(self.graph)
Z
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3731
        return [IrNode(n) for n in ordered_nodes]
W
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3732 3733

    def build_adjacency_list(self):
3734 3735 3736 3737
        """
        Build an adjacency list of operations for the `graph`.

        Returns:
3738
            dict{IrNode: set(IrNode)}: the adjacency list.
3739
        """
3740 3741 3742 3743 3744
        adj_list = core.build_adjacency_list(self.graph)
        wrapped_adj_list = dict()
        for k, v in six.iteritems(adj_list):
            wrapped_adj_list[IrNode(k)] = {IrNode(n) for n in v}
        return wrapped_adj_list
W
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3746 3747 3748 3749 3750 3751 3752 3753
    def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
        """
        Draw the graph. If `dot` command is installed, the drawn graph
        will be saved as pdf file type, otherwise dot file type is used.

        Args:
            save_path(str): the save path of drawn graph.
            name(str): the name of drawn graph.
3754
            marked_nodes(set(IrNode)): nodes that are needed to be marked.
3755 3756 3757 3758 3759
            Default value is None.
            remove_ctr_var(bool): If it is set True, all control variable nodes
            in the graph will be removed. Default value is True.
        """

3760 3761 3762
        def _convert_to_pdf(dot_file_path):
            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
3763
                                          + ' -o ' + pdf_save_path, shell=True)
3764 3765 3766 3767 3768
            if exited_code != 0:
                print('The dot command is needed for creating pdf files.')
                print('The {} is saved as the dot filetype.'.format(
                    dot_file_path))

3769
        remove_ctr_vars = set()
3770
        if remove_ctr_var:
3771
            for node in self.all_var_nodes():
3772 3773 3774
                if node.is_ctrl_var():
                    remove_ctr_vars.add(node)
            self.safe_remove_nodes(remove_ctr_vars)
3775 3776
        print('Total ops num = {}.'.format(len(self.all_op_nodes())))

3777 3778
        if marked_nodes is not None:
            if not isinstance(marked_nodes, set):
3779 3780 3781 3782 3783 3784
                if isinstance(marked_nodes, Iterable):
                    marked_nodes = set(marked_nodes)
                else:
                    marked_nodes = {marked_nodes}
            marked_nodes = {n.node for n in marked_nodes}
            remove_ctr_vars = {n.node for n in remove_ctr_vars}
3785 3786 3787 3788
            marked_nodes = marked_nodes - remove_ctr_vars
            if self.graph.has('__graphviz__marked_node__'):
                self.graph.erase('__graphviz__marked_node__')
            self.graph.set('__graphviz__marked_node__', marked_nodes)
3789 3790
        if not os.path.exists(save_path):
            os.makedirs(save_path)
3791 3792 3793 3794 3795 3796 3797
        viz_dot_path = os.path.join(save_path, name) + '.dot'
        viz_pass = core.get_pass('graph_viz_pass')
        viz_pass.set('graph_viz_path', viz_dot_path)
        viz_pass.apply(self.graph)
        _convert_to_pdf(viz_dot_path)

    def to_program(self):
3798 3799 3800
        """
        Convert the graph into a Program.

Z
Zhen Wang 已提交
3801
        WARN: When the graph includes backward operator nodes, the
3802 3803 3804 3805 3806 3807
        conversion process may be failed. Usually, this function is
        only used to convert a test graph.

        Returns:
            Program: a program converted from the graph.
        """
3808
        convert_pass = core.get_pass('graph_to_program_pass')
3809 3810
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
3811 3812 3813 3814
        convert_pass.apply(self.graph)
        program = Program._construct_from_desc(desc)
        return program

3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825
    def _find_node_by_name(self, nodes, node_name):
        """
        Find a node in the giving nodes set by the name.
        """
        target_node = None
        for n in nodes:
            if n.name() == node_name:
                target_node = n
        assert target_node is not None, "Cannot find the target node in the giving set."
        return target_node

3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841
    def _update_desc_attr(self, desc, name, val):
        """
        Update the value of desc's attribute by attribute's name.
        """
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and all(
                isinstance(v, Block) for v in val):
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
                isinstance(val, core.ProgramDesc):
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)


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3842
class Program(object):
D
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3843
    """
3844 3845
    Create Python Program.  It has at least one :ref:`api_guide_Block_en`, when the
    control flow op like conditional_block, while :ref:`api_fluid_layers_While` is included,
J
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3846
    it will contain nested block.
3847

J
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3848 3849 3850
    Please reference the
    `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_
    for details.
D
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3851

J
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3852
    A set of Program usually contains startup program and main program.
J
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3853
    A startup program is set to contain some initial work, eg. initialize the ``Parameter``, and the main
J
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3854 3855 3856 3857 3858 3859 3860
    program will contain the network structure and vars for train.

    A set of Program can be used for test or train, in train program ,
    Paddle will contain all content to build a train network,  in test
    program Paddle will prune some content which is irrelevant to test, eg.
    backward ops and vars.

J
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3861 3862 3863 3864
    **Notes**:
        **we have** :ref:`api_fluid_default_startup_program` **and** :ref:`api_fluid_default_main_program`
        **by default, a pair of them will shared the parameters. The** :ref:`api_fluid_default_startup_program` **only run once to initialize parameters,**
        :ref:`api_fluid_default_main_program` **run in every mini batch and adjust the weights.**
D
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3865 3866

    Returns:
J
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3867
        Program: An empty Program.
D
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3868 3869

    Examples:
3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882
        .. code-block:: python

            import paddle.fluid as fluid

            main_program = fluid.Program()
            startup_program = fluid.Program()
            with fluid.program_guard(main_program=main_program, startup_program=startup_program):
                x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
                y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
                z = fluid.layers.fc(name="fc", input=x, size=10, act="relu")

            print("main program is: {}".format(main_program))
            print("start up program is: {}".format(startup_program))
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3883 3884 3885

    """

3886 3887
    def __init__(self):
        self.desc = core.ProgramDesc()
Y
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3888 3889
        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0
3890 3891
        global global_prog_seed
        self._seed = global_prog_seed
Y
yuyang18 已提交
3892
        self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
3893
        self.__op_role_var = []
T
tangwei12 已提交
3894

3895 3896
        # for distribute training
        # _is_distributed = True if under distributed training
T
tangwei12 已提交
3897
        self._is_distributed = False
3898
        # _is_chief = True if the trainer is the first one, usually No.0
T
tangwei12 已提交
3899
        self._is_chief = False
3900 3901 3902
        # _parameters_on_pservers records all the parameters distributed on parameter servers.
        self._parameters_on_pservers = None
        # _endpoints is a list about parameter servers ip:port, such as ["ip:port","ip:port"]
T
tangwei12 已提交
3903
        self._endpoints = []
3904 3905 3906
        # if current role is parameter server, the _ps_endpoint is its "ip:port"
        self._ps_endpoint = None
        # trainers_endpoints, it is used for distribution.
3907
        self._trainers_endpoints = []
3908
        # the distributed lookup table names
T
tangwei12 已提交
3909
        self._distributed_lookup_table = None
3910 3911 3912

        # use Deep gradient comrepssion or not
        self._enable_dgc = False
3913 3914
        self._use_lamb = False

3915 3916 3917
        self._nccl_comm_num = 1
        self._use_hierarchical_allreduce = False
        self._hierarchical_allreduce_inter_nranks = 0
3918

3919 3920 3921
        # if this program has been optimized by distributed optimizer
        # fleet_opt will be given a value
        self._fleet_opt = None
D
dongdaxiang 已提交
3922
        self._program_config = None
3923

H
hutuxian 已提交
3924 3925 3926
        # assigned if this program has been parsed by a pipeline optimizer
        self._pipeline_opt = None

3927 3928 3929
        # appending gradients times
        self._appending_grad_times = 0

3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956
    def global_seed(self, seed=0):
        """
        Set global seed for Program

        Returns:
            None.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                print(prog.random_seed)
                ## 0
                ## the default random seed is 0

                prog.global_seed(102)
                prog1 = fluid.default_main_program()
                print(prog1.random_seed)
                ## 102
                ## the random seed is 102
        """
        global global_prog_seed
        global_prog_seed = seed
        self._seed = global_prog_seed

Y
yuyang18 已提交
3957
    @property
3958
    def _op_role(self):
Y
yuyang18 已提交
3959 3960 3961 3962 3963 3964 3965 3966
        """
        The operator role. In a enum {Forward, Backward, Optimize}.

        Notes: this is a low level API. It is used only for ParallelExecutor to
        duplicate or schedule operator to devices.

        For example, the forward operator should be executed on every device.
        The backward operator should be executed on every device and the
3967
        parameter gradient of backward (use :code:`_op_role_var` to get this
Y
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3968 3969 3970 3971
        variable) operator should be merged to one device. The optimization
        operators should be executed on only one device and broadcast the
        optimization result, i.e., the new parameter, to every other device.
        """
Y
yuyang18 已提交
3972 3973
        return self._current_role

3974 3975
    @_op_role.setter
    def _op_role(self, role):
Y
yuyang18 已提交
3976 3977 3978
        self._current_role = role

    @property
3979
    def _op_role_var(self):
Y
yuyang18 已提交
3980
        """
3981
        The auxiliary variables for :code:`_op_role` property.
Y
yuyang18 已提交
3982

3983
        See Also: :code:`Program._op_role`'s documentation for details.
Y
yuyang18 已提交
3984 3985 3986

        Notes: This is a very low-level API. Users should not use it directly.
        """
3987
        return self.__op_role_var
Y
yuyang18 已提交
3988

3989
    @signature_safe_contextmanager
3990 3991 3992 3993 3994
    def _backward_role_guard(self):
        tmp_role = self._current_role

        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Backward
3995 3996 3997 3998
        try:
            yield
        finally:
            self._current_role = tmp_role
3999

S
rename  
sneaxiy 已提交
4000
    @signature_safe_contextmanager
W
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4001
    def _optimized_guard(self, param_and_grads):
Y
yuyang18 已提交
4002 4003 4004 4005 4006 4007 4008
        """
        A with guard to set :code:`Optimization` :code:`OpRole` and
        :code:`OpRoleVar` automatically.

        Notes: This is a very low level API. Users should not use it directly.

        Args:
4009
            param_and_grads(list): The variables (names) to be optimized.
Y
yuyang18 已提交
4010 4011 4012

        Examples:

4013
            >>> import paddle.fluid as fluid
Y
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4014
            >>> p, g = backward(...)
W
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4015
            >>> with program._optimized_guard([p,g]):
Y
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4016 4017
            >>>     p = p - 0.001 * g
        """
X
Xin Pan 已提交
4018
        tmp_role = self._current_role
4019
        tmp_var = self.__op_role_var
X
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4020

Y
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4021 4022
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Optimize
4023
        self.__op_role_var = [
4024 4025 4026
            var.name if isinstance(var, Variable) else var
            for var in param_and_grads
        ]
4027 4028 4029 4030 4031
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
Y
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4032

S
rename  
sneaxiy 已提交
4033
    @signature_safe_contextmanager
X
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4034
    def _lr_schedule_guard(self, is_with_opt=False):
4035 4036 4037 4038 4039 4040 4041
        """
        A with guard to set :code:`LRSched` :code:`OpRole` and
        :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
        set to the target learning rate.

        Notes: This is a very low level API. Users should not use it directly.

X
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4042 4043 4044 4045
        Args:
            is_with_opt: Only set to true if these ops a in the middle
                 of a bunch of optimize ops so that it can be treated
                 correctly. For example, sgd->lr_op->sgd->lr_op->sgd.
4046 4047 4048

        Examples:

4049
            >>> import paddle.fluid as fluid
4050 4051 4052 4053
            >>> p, g = backward(...)
            >>> with program.lr_schedule_guard():
            >>>     lr = lr * decay
        """
4054 4055

        tmp_role = self._current_role
4056
        tmp_var = self.__op_role_var
4057

4058 4059
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.LRSched
X
Xin Pan 已提交
4060 4061
        if is_with_opt:
            self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
4062
        # TODO(typhoonzero): how to set target learning rate var
4063
        self.__op_role_var = []
4064 4065 4066 4067 4068
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
4069

4070
    def __str__(self):
Y
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4071 4072 4073 4074 4075 4076 4077 4078 4079
        """
        Get the protobuf debug string of this Program.

        Returns:
            (str): The protobuf debug string.

        Raises:
            ValueError: If any of required fields is not set.
        """
4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118
        return self._to_readable_code()

    def _to_readable_code(self, skip_op_callstack=True):
        """
        Get readable debug string of Program.

        .. note::
            If you want to get the debug string in protobuf format,
            please use :code:`to_string` method.

        Args:
            skip_op_callstack(bool): whether to skip parsing Operator's attribute
                op_callstack, default value is True

        Returns:
            string: The formatted Program string.

        Examples:
            .. code-block:: python

            import paddle.fluid as fluid

            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_var = cur_block.create_var(name="X",
                                           shape=[-1, 23, 48],
                                           dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [new_var]},
                                outputs={"Out": [new_var]})
            print(cur_program._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
        ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
            type(skip_op_callstack))
        program_str = ""
        for block in self.blocks:
            program_str += block._to_readable_code(skip_op_callstack)
4119
            program_str += '\n'
4120
        return program_str
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    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
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        Args:

            throw_on_error (bool): raise Value error when any of required fields is not set.
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            with_details (bool): True if more details about variables and parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need to print.
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        Returns:
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            str: The debug string describe current Program.
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        Raises:
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            ValueError: If any of required fields is not set and throw_on_error is True.
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4138 4139 4140 4141 4142 4143
        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
4144 4145
                x = fluid.layers.data(name="X", shape=[2,3], dtype="float32", append_batch_size=False)
                pred = fluid.layers.fc(x, size=3)
4146
                prog_string = prog.to_string(throw_on_error=True, with_details=False)
4147
                prog_string_with_details = prog.to_string(throw_on_error=False, with_details=True)
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                print("program string without detail: {}".format(prog_string))
4149
                print("program string with detail: {}".format(prog_string_with_details))
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        """
4151 4152 4153 4154 4155 4156 4157 4158 4159
        assert isinstance(
            throw_on_error, bool
        ), "The type of throw_on_error parameter is wrong, expected bool, but received {}.".format(
            type(throw_on_error))
        assert isinstance(
            with_details, bool
        ), "The type of with_details parameter is wrong, expected bool, but received {}.".format(
            type(with_details))

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        if with_details:
            res_str = ""
            for block in self.blocks:
                res_str += block.to_string(throw_on_error, with_details)
        else:
            protostr = self.desc.serialize_to_string()
4166 4167
            proto = framework_pb2.ProgramDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
4170

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    def _get_desc(self):
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        """
        Get the C++ side of `ProgramDesc` object pointer. The C++ object is
        exposed by :code:`pybind`.

        Notes: This is a very low level API. Users should not use this API
        directly.
        """
4179 4180
        return self.desc

X
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    def _version(self):
        return self.desc._version()

4184
    def clone(self, for_test=False):
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        """
4186
        **Notes**:
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            **1.** :code:`Program.clone()` **method DOES NOT clone** :ref:`api_fluid_io_DataLoader` .

            **2. Recommend you to use** :code:`clone` **before using** :code:`Opimizer.minimize`.

4191
            **3. This API has no effect in Dygraph Mode**
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4193
        Create a new Program with forward content of original one when ``for_test=True``.
4194
        Create a new Program as same as the original one when ``for_test=False``.
4195

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        Some operators, e.g., :ref:`api_fluid_layers_batch_norm` , behave differently between
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        training and testing. They have an attribute, :code:`is_test`, to
        control this behaviour. This method will change the :code:`is_test`
        attribute of them to :code:`True` when :code:`for_test=True`.
4200

4201 4202
        * Set for_test to False when you want to clone the program for training.
        * Set for_test to True when you want to clone the program for testing.
4203 4204
          We will prune the backward and optimize part of the program when you
          use :code:`clone` after :code:`Opimizer.minimize`, but we still
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          recommend you to use :code:`clone` before using :code:`Opimizer.minimize`.
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        For Example:
4208
          ::
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4210 4211 4212 4213 4214 4215 4216 4217
            import paddle.fluid as fluid
            img = fluid.layers.data(name='image', shape=[784])
            pred = fluid.layers.fc(input=img, size=10, act='relu')
            loss = fluid.layers.mean(pred)
            # Here we use clone before Momentum
            test_program = fluid.default_main_program().clone(for_test=True)
            optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
            optimizer.minimize(loss)
4218

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        Args:
4220

4221 4222
            for_test (bool): True if change the :code:`is_test` attribute of operators to :code:`True`
                and prune the backward and optimize part of the program. The default value is :code:`False` .
4223

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        Returns:
4225
            Program: A new Program with forward content of original one when ``for_test=True``.  A new Program as same as the original one when ``for_test=False``
4226

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        Examples:

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        **Notes: The Program's order maybe different after** :code:`clone` **and
4231
        this will not affect your training or testing progress. In the following
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        example we give you an simple method** :code:`print_prog(program)` **to
4233
        print Program Descs inorder to make sure you have same print result
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        after** :code:`clone`:
4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270
            .. code-block:: python

                import paddle.fluid as fluid
                import six

                def print_prog(prog):
                    for name, value in sorted(six.iteritems(prog.block(0).vars)):
                        print(value)
                    for op in prog.block(0).ops:
                        print("op type is {}".format(op.type))
                        print("op inputs are {}".format(op.input_arg_names))
                        print("op outputs are {}".format(op.output_arg_names))
                        for key, value in sorted(six.iteritems(op.all_attrs())):
                            if key not in ['op_callstack', 'op_role_var']:
                                print(" [ attrs: {}:   {} ]".format(key, value))


        1. To clone a test program, the sample code is:
                .. code-block:: python

                    import paddle.fluid as fluid
                    import six

                    def print_prog(prog):
                        for name, value in sorted(six.iteritems(prog.block(0).vars)):
                            print(value)
                        for op in prog.block(0).ops:
                            print("op type is {}".format(op.type))
                            print("op inputs are {}".format(op.input_arg_names))
                            print("op outputs are {}".format(op.output_arg_names))
                            for key, value in sorted(six.iteritems(op.all_attrs())):
                                if key not in ['op_callstack', 'op_role_var']:
                                    print(" [ attrs: {}:   {} ]".format(key, value))

                    train_program = fluid.Program()
                    startup_program = fluid.Program()
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                    # startup_program is used to do some parameter init work,
                    # and main program is used to hold the network
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                    with fluid.program_guard(train_program, startup_program):
                        with fluid.unique_name.guard():
                            img = fluid.layers.data(name='image', shape=[784])
                            hidden = fluid.layers.fc(input=img, size=200, act='relu')
                            hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
                            loss = fluid.layers.cross_entropy(
                                                      input=fluid.layers.fc(hidden, size=10, act='softmax'),
                                        label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
                            avg_loss = fluid.layers.mean(loss)
4283
                            test_program = train_program.clone(for_test=True)
4284
                    print_prog(test_program)
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                    # Due to parameter sharing usage for train and test, so we need to use startup program of train
                    # instead of using test startup program, while nothing is in test's startup program

                    # In Paddle Fluid we will share weights by using the same Variable name. In train and test program
                    # all parameters will have the same name and this can make train and test program sharing parameters,
                    # that's why we need to use startup program of train. And for startup program of test, it has nothing,
                    # since it is a new program.

4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313 4314 4315
                    with fluid.program_guard(train_program, startup_program):
                        with fluid.unique_name.guard():
                            sgd = fluid.optimizer.SGD(learning_rate=1e-3)
                            sgd.minimize(avg_loss)


        2. The clone method can be avoid if you create program for training and program for testing individually.
                .. code-block:: python

                    import paddle.fluid as fluid
                    import six

                    def print_prog(prog):
                        for name, value in sorted(six.iteritems(prog.block(0).vars)):
                            print(value)
                        for op in prog.block(0).ops:
                            print("op type is {}".format(op.type))
                            print("op inputs are {}".format(op.input_arg_names))
                            print("op outputs are {}".format(op.output_arg_names))
                            for key, value in sorted(six.iteritems(op.all_attrs())):
                                if key not in ['op_callstack', 'op_role_var']:
                                    print(" [ attrs: {}:   {} ]".format(key, value))
4316 4317
                    
                    def network():
4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331
                        img = fluid.layers.data(name='image', shape=[784])
                        hidden = fluid.layers.fc(input=img, size=200, act='relu')
                        hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
                        loss = fluid.layers.cross_entropy(
                            input=fluid.layers.fc(hidden, size=10, act='softmax'),
                            label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
                        avg_loss = fluid.layers.mean(loss)
                        return avg_loss

                    train_program_2 = fluid.Program()
                    startup_program_2 = fluid.Program()
                    test_program_2 = fluid.Program()
                    with fluid.program_guard(train_program_2, startup_program_2):
                        with fluid.unique_name.guard():
4332 4333 4334
                            avg_loss = network()
                            sgd = fluid.optimizer.SGD(learning_rate=1e-3)
                            sgd.minimize(avg_loss)
4335
                    # the test startup program is not used.
4336
                    with fluid.program_guard(test_program_2, startup_program_2):
4337
                        with fluid.unique_name.guard():
4338 4339
                            avg_loss = network()
                    print_prog(test_program_2)
4340 4341

        The two code snippets above will generate and print same programs.
4342
        """
4343 4344 4345 4346 4347

        #NOTE(zhiqiu): we sync the original program first, since its program may diff with
        # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
        self._sync_with_cpp()

4348
        pruned_origin_block_id_map = None
4349
        if for_test:
4350 4351 4352 4353 4354 4355 4356 4357 4358
            forward_prog = Program()
            forward_prog.desc, pruned_origin_block_id_map = core.prune_backward(
                self.desc)
            forward_prog.blocks = [
                Block(forward_prog, i)
                for i in six.moves.range(forward_prog.desc.num_blocks())
            ]
            forward_prog._sync_with_cpp()
            p = forward_prog._inference_optimize(prune_read_op=False)
4359
        else:
4360
            p = Program()
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            p.current_block_idx = self.current_block_idx
            p._seed = self._seed
4363
            p.desc = core.ProgramDesc(self.desc)
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            p.blocks = [
                Block(p, i) for i in six.moves.range(self.desc.num_blocks())
            ]
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            p._current_role = self._current_role
4369
            p.__op_role_var = self.__op_role_var
4370
            p._appending_grad_times = self._appending_grad_times
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4372 4373
            #NOTE(zhiqiu): we sync the cloned program, to update its program by
            # its desc.
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            p._sync_with_cpp()
4375

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        p._copy_param_info_from(self)
4377
        p._copy_data_info_from(self, pruned_origin_block_id_map)
4378
        p._copy_dist_param_info_from(self)
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        return p
4380

4381
    def _prune(self, targets):
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        """
        Prune operators and variables which are not needed to generate
        :code:`targets`.

        Notes: This is a very low level API. Users should not use this API
        directly. This API is in flux and not stable.

        Args:
4390
            targets(list|Variable|Operator): A list of variables, operators, or variable names
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                need to be pruned

        Returns:
            Program:  A new, pruned program.
4395
        """
4396
        return self._prune_with_input([], targets)
4397 4398

    def _prune_with_input(self, feeded_var_names, targets):
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        """
4400 4401 4402 4403 4404 4405 4406 4407 4408 4409
        Prune operators and variables which are not needed to generate
        :code:`targets`. Prune operators and variables which are needed 
        to generate feeded_var 

        Notes: This is a very low level API. Users should not use this API
        directly. This API is in flux and not stable.

        Args:
            feeded_var_names(list|str): A list of variable names from where
                pruning start. If it is set as [], this API works just like _prune()
4410
            targets(list|Variable|Operator): A list of variables, operators, or variable names
4411 4412 4413 4414 4415 4416
                need to be pruned

        Returns:
            Program:  A new, pruned program.
        """

4417 4418 4419 4420
        #NOTE(zhiqiu): we sync the original program first, since its program may diff with
        # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
        self._sync_with_cpp()

4421 4422
        if not isinstance(feeded_var_names, list):
            feeded_var_names = [feeded_var_names]
4423 4424
        if not isinstance(targets, list):
            targets = [targets]
4425 4426 4427

        for var in feeded_var_names:
            if not isinstance(var, six.string_types):
4428 4429 4430
                raise ValueError(
                    "All feeded_var_names of Program._prune_with_input() can only be "
                    "str, but received %s." % type(var))
4431

4432 4433 4434 4435
        targets_idx = []
        for t in targets:
            if not isinstance(t, Operator):
                if isinstance(t, Variable):
4436 4437 4438
                    name = t.name
                elif isinstance(t, six.string_types):
                    name = str(t)
4439
                else:
4440 4441 4442
                    raise ValueError(
                        "All targets of Program._prune_with_input() can only be "
                        "Variable or Operator, but received %s." % type(t))
4443 4444 4445 4446 4447 4448 4449 4450

                # NOTEZ(zhiqiu): For variable to be fed in fetch_list, there two cases:
                # (1) the variable is leaf, it has no op that generates it;
                # (2) the variable is not leaf, and we need to prune the op that generates it.
                # In both cases, wo can just skip target_op of that it.
                if name in feeded_var_names:
                    continue

4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466
                # After transpiler processing, the op that output this
                # variable maybe has been changed, so t.op is not reliable
                # and we need to find the current op that generate this
                # variable here.
                target_op = None
                global_block = self.global_block()
                for idx, op in enumerate(global_block.ops):
                    if name in op.output_arg_names:
                        # NOTE(zhiqiu): Find op that generate target name.
                        # Skip optimize op except for optimize op in targets, 
                        # since optimize op generates parameters.
                        if op._is_optimize_op() and op not in targets:
                            continue
                        else:
                            target_op = op
                            break
4467 4468 4469 4470 4471 4472 4473 4474
                if target_op is None:
                    raise ValueError(
                        "The target variable used for pruning should have an "
                        "associated operator that generates it.")
                else:
                    targets_idx.append([target_op.block.idx, target_op.idx])
            else:
                targets_idx.append([t.block.idx, t.idx])
4475

4476
        res = Program()
4477 4478 4479
        res.desc, pruned_origin_block_id_map = core.prune(self.desc,
                                                          set(feeded_var_names),
                                                          targets_idx)
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        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
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        res._sync_with_cpp()
4484 4485 4486 4487 4488

        res._copy_param_info_from(self)
        res._copy_data_info_from(self, pruned_origin_block_id_map)
        res._copy_dist_param_info_from(self)

4489 4490
        return res

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    def _inference_optimize(self, prune_read_op=True):
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        """
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        This method will create a new program and do following adjustments on it:
        1. Remove all reader variables and their creator ops if exist.

        2. Remove the :code:`read_op` if exists.

4498
        3. change the :code:`is_test`
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        attribute of operators to :code:`True`. All the :code:`Parameter`
        information will be lost.

4502
        Args:
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4503 4504
            prune_read_op(bool): remove the read ops that are added by py_reader
                                 for cpp inference library
4505

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        Notes: This API is a very low level API. Use
        :code:`Program.clone(for_test=True)` instead.

        Returns:
            Program: The new program.
        """
4512
        res = Program()
4513
        res.desc = core.ProgramDesc(self.desc)
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        # remove all readers and the read_op if exist
        read_op_idx = 0
        root_block = res.desc.block(0)
X
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4518
        if prune_read_op:
4519 4520 4521 4522 4523 4524 4525 4526 4527
            while True:
                if read_op_idx >= root_block.op_size() or root_block.op(
                        read_op_idx).type() == 'read':
                    break
                read_op_idx += 1
            if read_op_idx < root_block.op_size():
                root_block._remove_op(0, read_op_idx + 1)
            for var in root_block.all_vars():
                if var.type() == core.VarDesc.VarType.READER:
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                    root_block._remove_var(cpt.to_bytes(var.name()))
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4529 4530

        # change all `is_test` attributes to True
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        for i in six.moves.range(res.desc.num_blocks()):
4532
            block = res.desc.block(i)
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            for j in six.moves.range(block.op_size()):
4534 4535
                op = block.op(j)
                if op.has_attr('is_test'):
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                    op._set_attr('is_test', True)
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4537 4538 4539
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
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        res._sync_with_cpp()
4541 4542
        return res

4543 4544
    @staticmethod
    def parse_from_string(binary_str):
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        """
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4546 4547 4548 4549
        **Notes**:
            **1. All information about parameters will be lost after serialization**

            **2. This API has no effect in Dygraph mode**
Y
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4551 4552
        Deserialize a Program from  `protobuf <https://en.wikipedia.org/wiki/Protocol_Buffers>`_  binary string.
        This method always use to save and load model
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        Args:
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            binary_str_type (str): the binary prootbuf string.
4557

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        Returns:
            Program: A deserialized Program.
4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                startup_prog = fluid.Program()
                main_prog = fluid.Program()
                with fluid.program_guard(startup_prog, main_prog):
                    x = fluid.layers.data(
                        name='X', shape=[1000, 784], dtype='float32', append_batch_size=False)

                    y = fluid.layers.data(
                        name='Y', shape=[784, 100], dtype='float32', append_batch_size=False)

                    z = fluid.layers.mul(x=x, y=y)

                    binary_str = fluid.default_main_program().desc.serialize_to_string()
                    prog_restored = fluid.default_main_program().parse_from_string(binary_str)

                    print(fluid.default_main_program())
                    print(prog_restored)
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        """
4583 4584
        p = Program()
        p.desc = core.ProgramDesc(binary_str)
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        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
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4586
        p._sync_with_cpp()
4587
        return p
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4589
    @staticmethod
4590
    def _construct_from_desc(desc):
4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603 4604 4605
        """
        Construct a program from program desc.

        Args:
            desc(core.ProgramDesc): The program desc for constructing.

        Returns:
            Program: A program.
        """
        p = Program()
        p.desc = desc
        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
        p._sync_with_cpp()
        return p

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    @property
    def random_seed(self):
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4608
        """
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4609
        The default random seed for random operators in Program. ``0`` means get
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4610 4611
        the random seed from random device.

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4612 4613 4614 4615
        **Notes: It must be set before the operators have been added.**

        Returns:
            int64: Random seed in current Program
4616

4617 4618 4619 4620 4621 4622 4623 4624

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                random_seed = prog.random_seed
4625
                x_var = fluid.layers.data(name="X", shape=[3,3], dtype="float32", append_batch_size=False)
4626 4627 4628
                print(random_seed)
                ## 0
                ## the default random seed is 0
4629 4630

                # Here we need to set random seed before we use fluid.layers.dropout
4631
                prog.random_seed = 1
4632 4633
                z_var = fluid.layers.dropout(x_var, 0.7)

4634
                print(prog.random_seed)
4635 4636
                ## 1
                ## the random seed is change to 1
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4637
        """
D
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        return self._seed

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    @property
    def num_blocks(self):
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4642
        """
4643 4644
        The number of :ref:`api_guide_Block_en`  in this Program.

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        **Notes: This API has no effect in Dygraph mode**

        Returns:
            int(Platform-dependent size): num of :ref:`api_guide_Block_en`  in current Program
4649

4650 4651 4652 4653 4654 4655 4656 4657 4658

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                num_blocks = prog.num_blocks
                print(num_blocks)
4659 4660


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        """
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        return self.desc.num_blocks()

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    @random_seed.setter
    def random_seed(self, seed):
        if not isinstance(seed, int):
4667 4668 4669
            raise ValueError(
                "Program.random_seed's input seed must be an integer, but received %s."
                % type(seed))
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        self._seed = seed

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    def __repr__(self):
4673
        return self.__str__()
4674

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4675
    def global_block(self):
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4676
        """
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4677 4678
        **Notes**:
            **This API has no effect in Dygraph mode**
4679 4680 4681

        Get the first :ref:`api_guide_Block_en` of this Program.

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        Returns:
            :ref:`api_guide_Block_en`: The first :ref:`api_guide_Block_en`  of this Program.
4684

4685 4686 4687 4688 4689 4690 4691 4692 4693

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                gb_block = prog.global_block()
                print(gb_block)
4694

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        """
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4696 4697
        return self.blocks[0]

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    def block(self, index):
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        """
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        **Notes**:
            **This API has no effect in Dygraph mode**
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4702

4703 4704
        Get the :code:`index`  :ref:`api_guide_Block_en`  of this Program

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        Args:
            index (int) - The index of  :ref:`api_guide_Block_en`  to get
4707

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        Returns:
            :ref:`api_guide_Block_en`: The :code:`index` block
4710 4711 4712 4713 4714 4715 4716 4717 4718

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                block_0 = prog.block(0)
                print(block_0)
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        """
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4720 4721
        return self.blocks[index]

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    def current_block(self):
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4723
        """
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4724 4725
        **Notes**:
            **This API has no effect in Dygraph mode**
4726

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4727 4728
        Get the current  :ref:`api_guide_Block_en` . The :code:`current`  :ref:`api_guide_Block_en`
        is the  :ref:`api_guide_Block_en`  to append operators.
4729

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4730 4731
        Returns:
             :ref:`api_guide_Block_en`: The :code:`index`  :ref:`api_guide_Block_en`
4732

4733 4734 4735 4736 4737 4738 4739 4740
        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                current_blk = prog.current_block()
                print(current_blk)
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        """
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        return self.blocks[self.current_block_idx]

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    def _create_block(self, parent_idx=None):
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        """
        Create a new block with the :code:`parent_idx` and change the current block
        to new block.

        Args:
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            parent_idx(int): The parent block index.

        Returns:
            Block: The new block.
        """
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        new_block_idx = len(self.blocks)
F
update  
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        parent = self.current_block() if parent_idx is None else self.block(
            parent_idx)
        self.desc.append_block(parent.desc)
Y
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        self.current_block_idx = new_block_idx
        self.blocks.append(Block(self, self.current_block_idx))
        return self.current_block()

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    def _rollback(self):
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        """
        Exit a code block, i.e., roll back to the parent block.
        Returns:
            None
        """
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        self.current_block_idx = self.current_block().parent_idx

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4772
    def _sync_with_cpp(self):
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        """
        Synchronize Python instance to its binding C++ object instance.
        If the program is modified in C++ space, this method should be invoked.

        Notes: This is a very low level API. Users should not invoke it
        directly.

        Returns:
            None
        """
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        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
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            block._sync_with_cpp()
Q
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4787

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4788
    def _copy_param_info_from(self, other):
4789
        """
4790
        Copy the information of parameters from other program.
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        Notes: This is a very low level API. Users should not invoke it
        directly.

4795 4796 4797 4798 4799 4800 4801
        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
4802 4803 4804
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
4805

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4806
        self.global_block()._copy_param_info_from(other.global_block())
4807

4808 4809 4810 4811 4812 4813 4814 4815 4816 4817 4818
    def _copy_dist_param_info_from(self, other):
        """
        Copy the information of distributed information from other program.

        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
4819 4820 4821
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
4822 4823
        self._is_distributed = other._is_distributed
        self._is_chief = other._is_chief
4824
        self._parameters_on_pservers = other._parameters_on_pservers
4825
        self._endpoints = other._endpoints
4826
        self._ps_endpoint = other._ps_endpoint
4827 4828
        self._distributed_lookup_table = other._distributed_lookup_table

4829
    def _copy_data_info_from(self, other, pruned_origin_block_id_map=None):
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        """
        Copy the information of data variables from other program.
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4832

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        Notes: This is a very low level API. Users should not invoke it
        directly.

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4836 4837
        Args:
            other(Program): Other program
4838 4839 4840 4841
            pruned_origin_block_id_map(dict{int:int}): A dict which maps the block id in program
            self to the block id in program other. For example, {0:0, 1:1, 2:3} means block 0 in self is 
            cloned from block 0 in other, etc. Default is None, which means default mapped, 
            {0:0, 1:1,..., n:n}.
F
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4842 4843 4844 4845 4846

        Returns:
            None
        """
        if not isinstance(other, Program):
4847 4848 4849
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
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4851 4852 4853 4854 4855
        if not pruned_origin_block_id_map:
            pruned_origin_block_id_map = {
                i: i
                for i in six.moves.range(self.desc.num_blocks())
            }
4856 4857 4858

        # NOTE(zhiqiu): All vars in cloned program exist in original program.
        # The reverse is not true, due to backward pruning.
4859 4860
        for i, block in enumerate(self.blocks):
            other_block = other.blocks[pruned_origin_block_id_map[i]]
4861
            for var in list(block.vars.values()):
4862 4863 4864 4865 4866 4867 4868
                other_var = other_block.var(var.name)
                if other_var.is_data:
                    var.is_data = True
                if other_var.desc.need_check_feed():
                    var.desc.set_need_check_feed(True)
                if other_var.stop_gradient:
                    var.stop_gradient = True
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4870
    def list_vars(self):
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4871
        """
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4872
        Get all :ref:`api_guide_Variable_en` from this Program. A iterable object is returned.
Y
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4873

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4874 4875
        Returns:
            iterable :ref:`api_guide_Variable_en`: The Generator will yield every variable in this program.
4876 4877 4878 4879 4880 4881 4882 4883 4884 4885 4886

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                img = fluid.layers.data(name='img', shape=[1,28,28], dtype='float32')
                label = fluid.layers.data(name='label', shape=[128,1], dtype='int64')
                for var in prog.list_vars():
                    print(var)
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4887
        """
4888
        for each_block in self.blocks:
4889
            for each_var in list(each_block.vars.values()):
4890 4891
                yield each_var

4892 4893 4894 4895 4896 4897 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940 4941 4942 4943 4944 4945 4946 4947 4948 4949
    def all_parameters(self):
        """
        Get all :ref:`api_guide_parameter_en` from this Program. A list object is returned.

        Returns:
            list[ :ref:`api_guide_parameter_en` ]: The list contians all parameters in this program.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                program = fluid.default_main_program()
                data = fluid.data(name='x', shape=[None, 13], dtype='float32')
                hidden = fluid.layers.fc(input=data, size=10)
                loss = fluid.layers.mean(hidden)
                fluid.optimizer.SGD(learning_rate=0.01).minimize(loss)

                for param in program.all_parameters():
                    print(param)

                # Here will print all parameters in current program, in this example,
                # the result is like:
                #
                # name: "fc_0.w_0"
                # type {
                #   type: LOD_TENSOR
                #   lod_tensor {
                #     tensor {
                #       data_type: FP32
                #       dims: 13
                #       dims: 10
                #     }
                #   }
                # }
                # persistable: true
                #
                # name: "fc_0.b_0"
                # type {
                # type: LOD_TENSOR
                # lod_tensor {
                #     tensor {
                #       data_type: FP32
                #       dims: 10
                #     }
                #   }
                # }
                # persistable: true
                #
                # Here print(param) will print out all the properties of a parameter,
                # including name, type and persistable, you can access to specific
                # property of a parameter, such as param.name, param.type
        """
        parameters = []
        for each_block in self.blocks:
            parameters.extend(each_block.all_parameters())
        return parameters

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4951
@six.add_metaclass(ParameterMetaClass)
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4952
class Parameter(Variable):
4953
    """
4954
    Parameter is derived from Variable. A parameter is a persistable
4955
    Variable, and will be updated by optimizers after each iteration.
4956
    The training of a neural network is essentially the updating of
4957 4958
    its parameters.

4959
    Relative to a general Variable, a Parameter has several its own
4960 4961
    member variables:

4962 4963 4964 4965 4966 4967 4968 4969 4970 4971
    Args:
        trainable(bool): True if the parameter need to be updated after
            iterations.
        optimize_attr(map): Parameter attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the parameter. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this parameter.
4972 4973
    """

4974 4975 4976 4977 4978 4979
    def __init__(self,
                 block,
                 shape,
                 dtype,
                 type=core.VarDesc.VarType.LOD_TENSOR,
                 **kwargs):
4980 4981 4982 4983 4984
        if shape is None:
            raise ValueError("The shape of Parameter should not be None")
        if dtype is None:
            raise ValueError("The dtype of Parameter should not be None")

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4985
        if len(shape) == 0:
4986 4987
            raise ValueError(
                "The dimensions of shape for Parameter must be greater than 0")
Y
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4988 4989 4990

        for each in shape:
            if each < 0:
4991 4992 4993
                raise ValueError(
                    "Each dimension of shape for Parameter must be greater than 0, but received %s"
                    % list(shape))
4994 4995

        Variable.__init__(
4996 4997 4998 4999 5000 5001 5002
            self,
            block,
            persistable=True,
            shape=shape,
            dtype=dtype,
            type=type,
            **kwargs)
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5003 5004 5005 5006
        self.trainable = kwargs.get('trainable', True)

        self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})

5007 5008
        self.regularizer = kwargs.get('regularizer', None)

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5009
        self.do_model_average = kwargs.get('do_model_average', None)
W
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5010

5011 5012
        self.is_distributed = False

F
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5013
    def __str__(self):
5014
        return self._to_readable_code()
F
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5015

F
update  
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5016 5017 5018
    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
D
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5019

F
update  
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5020 5021 5022 5023 5024 5025 5026 5027
        Args:
            throw_on_error(bool): raise exception when self is not initialized
                when throw_on_error is True
            with_details(bool): more details about variables and parameters
                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True

        Returns(str): The debug string.

5028 5029 5030 5031 5032 5033 5034 5035 5036
        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32')
                debug_str = prog.to_string(throw_on_error=True, with_details=False)
                print(debug_str)
F
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5037 5038 5039 5040 5041 5042
        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        if with_details:
            res_str = Variable.to_string(self, throw_on_error, True)
            additional_attr = ("trainable", "optimize_attr", "regularizer",
5043
                               "do_model_average")
F
update  
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5044
            for attr_name in additional_attr:
5045 5046
                res_str += "%s: %s\n" % (attr_name,
                                         cpt.to_text(getattr(self, attr_name)))
F
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5047 5048
        else:
            res_str = Variable.to_string(self, throw_on_error, False)
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5049 5050 5051 5052
        return res_str

    __repr__ = __str__

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5053

5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 5112 5113
class ParamBase(core.VarBase):
    """
    ParamBase is derived from VarBase( Which is the Variable in Dygraph Mode ). A ParamBase is a persistable
    VarBase, and will be updated by optimizers after each iteration.
    The training of a neural network is essentially the updating of
    its ParamBase.

    Relative to a general Variable, a ParamBase has several its own
    member variables:

    Args:
        trainable(bool): True if the ParamBase need to be updated after
            iterations.
        optimize_attr(map): ParamBase attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the ParamBase. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this ParamBase.
    """

    @dygraph_only
    def __init__(self, shape, dtype, **kwargs):
        if shape is None:
            raise ValueError("The shape of Parameter should not be None")
        if dtype is None:
            raise ValueError("The dtype of Parameter should not be None")

        if len(shape) == 0:
            raise ValueError(
                "The dimensions of shape for Parameter must be greater than 0")

        for each in shape:
            if each < 0:
                raise ValueError(
                    "Each dimension of shape for Parameter must be greater than 0, but received %s"
                    % list(shape))

        if dtype is not None:
            if not isinstance(dtype, core.VarDesc.VarType):
                dtype = convert_np_dtype_to_dtype_(dtype)

        name = kwargs.get('name', unique_name.generate('_param_base'))

        super(ParamBase, self).__init__(dtype
                                        if dtype else core.VarDesc.VarType.FP32,
                                        list(shape) if shape else [], name,
                                        core.VarDesc.VarType.LOD_TENSOR, True)

        self.trainable = kwargs.get('trainable', True)

        self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})

        self.regularizer = kwargs.get('regularizer', None)

        self.do_model_average = kwargs.get('do_model_average', None)

        self.is_distributed = False

5114
        # self.block = default_main_program().global_block()
5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141 5142 5143 5144

    def __str__(self):
        return self.to_string(True)

    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.

        Args:
            throw_on_error(bool): raise exception when self is not initialized
                when throw_on_error is True
            with_details(bool): more details about variables and parameters
                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True

        Returns(str): The debug string.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32')
                debug_str = prog.to_string(throw_on_error=True, with_details=False)
                print(debug_str)
        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        tensor = self.value().get_tensor()
        if tensor._is_initialized():
5145
            return 'Parameter: %s\n%s' % (self.name, str(tensor))
5146
        else:
5147
            return 'Parameter: %s, not initialized' % (self.name)
5148 5149 5150 5151

    __repr__ = __str__


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# program is a global instance.
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5153 5154
_main_program_ = Program()
_startup_program_ = Program()
5155

5156

5157
def default_startup_program():
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5158
    """
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5159 5160
    Get default/global startup program.

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5161 5162 5163
    The layer function in :ref:`api_fluid_layers` will create parameters, :ref:`api_paddle_data_reader_reader` ,
    `NCCL <https://developer.nvidia.com/nccl>`_ handles as global variables. The :code:`startup_program` will
    initialize them by the OPs in startup  :ref:`api_fluid_Program` . The  :ref:`api_fluid_layers`  function will
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5164 5165 5166
    append these initialization operators into startup program.

    This method will return the :code:`default` or the :code:`current` startup
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5167
    program. Users can use  :ref:`api_fluid_program_guard`  to switch :ref:`api_fluid_Program` .
5168

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    Returns: current default startup :ref:`api_fluid_Program`
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    Returns type: :ref:`api_fluid_Program`
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    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            main_program = fluid.Program()
            startup_program = fluid.Program()
            with fluid.program_guard(main_program=main_program, startup_program=startup_program):
                x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
                y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
                z = fluid.layers.fc(name="fc", input=x, size=10, act="relu")

                print("main program is: {}".format(fluid.default_main_program()))
                print("start up program is: {}".format(fluid.default_startup_program()))
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    """
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    return _startup_program_
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def default_main_program():
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    """
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    This API can be used to get ``default main program`` which store the 
    descriptions of ``op`` and ``variable``.
    
    For example ``z = fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add`` 
    ``op`` and a new ``z`` ``variable``, and they will be recorded in ``default main program`` 
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    The ``default_main_program`` is the default value for ``Program`` parameter in 
    a lot of ``fluid`` APIs. For example, the :code:`Executor.run()` will execute the
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    :code:`default_main_program` when the program is not specified.
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    If you want to replace the ``default main program``, you can use :ref:`api_fluid_program_guard`
    
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    Returns:
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        :ref:`api_fluid_Program`: a ``Program`` which holding the descriptions of ops and variables in the network.
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    Examples:
        ..  code-block:: python

            import paddle.fluid as fluid
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            # Sample Network:
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            data = fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
            label = fluid.data(name='label', shape=[None, 1], dtype='int64')
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            conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
            bn1 = fluid.layers.batch_norm(conv1, act='relu')
            pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
            conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None)
            bn2 = fluid.layers.batch_norm(conv2, act='relu')
            pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
            
            fc1 = fluid.layers.fc(pool2, size=50, act='relu')
            fc2 = fluid.layers.fc(fc1, size=102, act='softmax')
            
            loss = fluid.layers.cross_entropy(input=fc2, label=label)
            loss = fluid.layers.mean(loss)
            opt = fluid.optimizer.Momentum(
                learning_rate=0.1,
                momentum=0.9,
                regularization=fluid.regularizer.L2Decay(1e-4))
            opt.minimize(loss)
            
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            #print the number of blocks in the program, 1 in this case
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            print(fluid.default_main_program().num_blocks)
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            #print the description of variable 'image'
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            print(fluid.default_main_program().blocks[0].var('image'))
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    """
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    return _main_program_
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def switch_main_program(program):
    """
    Switch the main program to a new program.
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    Args:
        program(Program): The new main program

    Returns:
        Program: The previous main program
    """
    global _main_program_
    prev_program = _main_program_
    _main_program_ = program
    return prev_program


def switch_startup_program(program):
    """
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    Switch the startup program to a new program
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    Args:
        program(Program): The new startup program

    Returns:
        Program: The previous startup program
    """
    global _startup_program_
    prev_program = _startup_program_
    _startup_program_ = program
    return prev_program


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@signature_safe_contextmanager
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def program_guard(main_program, startup_program=None):
    """
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    Change the global main program and startup program with `"with"` statement.
    Layer functions in the Python `"with"` block will append operators and
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    variables to the new main programs.
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    Args:
        main_program(Program): New main program inside `"with"` statement.
        startup_program(Program, optional): New startup program inside `"with"` 
            statement. :code:`None` means not changing startup program, 
            default_startup_program is still used.
            Default: None.

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    Examples:
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       .. code-block:: python
       
         import paddle.fluid as fluid
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         main_program = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(main_program, startup_program):
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             data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
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             hidden = fluid.layers.fc(input=data, size=10, act='relu')
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    Notes: The temporary :code:`Program` can be used if the user does not need
    to construct either of startup program or main program.
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    Examples:
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       .. code-block:: python
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         import paddle.fluid as fluid

         main_program = fluid.Program()
         # does not care about startup program. Just pass a temporary value.
         with fluid.program_guard(main_program, fluid.Program()):
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             data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
    
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    """
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    from .data_feeder import check_type
    check_type(main_program, 'main_program', Program, 'fluid.program_guard')
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    main_program = switch_main_program(main_program)
    if startup_program is not None:
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        check_type(startup_program, 'startup_program', Program,
                   'fluid.program_guard')
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        startup_program = switch_startup_program(startup_program)
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    try:
        yield
    finally:
        switch_main_program(main_program)
        if startup_program is not None:
            switch_startup_program(startup_program)
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def _get_var(name, program=None):
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    """
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    Get a variable by name from the global block of a program.
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    Args:
        name(str): name of the variable
        program(Program|None): program object.
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        If None, default_global_program() will be used.
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    Returns:
        Variable
    """
    if program is None:
        program = default_main_program()
    assert isinstance(name, str)
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    assert isinstance(program, Program)
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    return program.global_block().var(name)
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@signature_safe_contextmanager
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def _dygraph_guard(tracer):
    global _dygraph_tracer_
    tmp_trace = _dygraph_tracer_
    _dygraph_tracer_ = tracer
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    core._switch_tracer(tracer)
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    try:
        yield
    finally:
        core._switch_tracer(tmp_trace)
        _dygraph_tracer_ = tmp_trace
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@signature_safe_contextmanager
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def _dygraph_place_guard(place):
    global _dygraph_current_expected_place_
    tmp_place = _dygraph_current_expected_place_
    _dygraph_current_expected_place_ = place
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    try:
        yield
    finally:
        _dygraph_current_expected_place_ = tmp_place
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def load_op_library(lib_filename):
    """
    Load a dynamic library, including custom operators and kernels.
    When library is loaded, ops and kernels registered in the library
    will be available in PaddlePaddle main process.
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    Please note, the type of custom operators can't have the same type
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    with the existing operators in the framework.

    Args:
        lib_filename (str): name of dynamic library.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            #fluid.load_op_library('custom_op.so')

    """
    core.load_op_library(lib_filename)
    OpProtoHolder.instance().update_op_proto()
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def switch_device(device):
    global _current_device
    pre_device = _current_device
    _current_device = device
    return pre_device


@signature_safe_contextmanager
def device_guard(device=None):
    """
    **Notes**:
        **The API only supports static mode.**

    A context manager that specifies the device on which the OP will be placed.

    Args:
        device(str|None): Specify the device to use in the context. It should be 'cpu' or 'gpu',
            When it is set to 'cpu' or 'gpu', all OPs created in the context will be
            placed on CPUPlace or CUDAPlace. When 'gpu' is set and the program runs on
            single-card, the device index will be the same as the device on which the
            executor runs. Default: None, OPs in this context will be automatically
            assigned devices.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            support_gpu = fluid.is_compiled_with_cuda()
            place = fluid.CPUPlace()
            if support_gpu:
                place = fluid.CUDAPlace(0)

            # if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0)
            data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32')
            data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32')
            shape = fluid.layers.shape(data2)

            with fluid.device_guard("cpu"):
                # Ops created here will be placed on CPUPlace
                shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4])
            with fluid.device_guard('gpu'):
                # if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace
                out = fluid.layers.crop_tensor(data1, shape=shape)

            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            result = exe.run(fetch_list=[out])
    """

    if device not in ['cpu', 'gpu', '', None]:
        raise ValueError(
            "The Attr(device) should be 'cpu' or 'gpu', and it can also be empty string or None "
            "when there is no need to specify device. But received %s" % device)
    pre_device = switch_device(device)
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    try:
        yield
    finally:
        switch_device(pre_device)
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def set_flags(flags):
    """
    This function sets the GFlags value in Paddle.

    Args:
        flags (dict): A dict contains flags and its value.

    Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                fluid.set_flags({'FLAGS_eager_delete_tensor_gb': 1.0})
    """
    if not isinstance(flags, dict):
        raise TypeError('flags in set_flags should be a dict')
    for key, value in flags.items():
        if core.globals().is_public(key):
            core.globals()[key] = value
        else:
            raise ValueError(
                "Flag %s cannot set its value through this function." % (key))


def get_flags(flags):
    """
    This function gets the GFlags value in Paddle.

    Args:
        flags(list|tuple|str): A list/tuple of string or a string which is the flag's name.

    Returns:
        flag's value in Paddle.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            flags = ['FLAGS_eager_delete_tensor_gb', 'FLAGS_check_nan_inf']
            res = fluid.get_flags(flags)
            print(res)
            # {'FLAGS_eager_delete_tensor_gb': 0.0, 'FLAGS_check_nan_inf': False}
    """
    flags_value = {}
    if isinstance(flags, (list, tuple)):
        for key in flags:
            if (core.globals().is_public(key)):
                value = core.globals()[key]
                temp = {key: value}
                flags_value.update(temp)
            else:
                raise ValueError(
                    'Flag %s cannot get its value through this function.' %
                    (key))
    elif isinstance(flags, str):
        if (core.globals().is_public(flags)):
            value = core.globals()[flags]
            temp = {flags: value}
            flags_value.update(temp)
        else:
            raise ValueError(
                'Flag %s cannot get its value through this function.' % (flags))
    else:
        raise TypeError('Flags in get_flags should be a list, tuple or string.')
    return flags_value