framework.py 196.3 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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import functools
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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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    'is_compiled_with_xpu',
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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
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_global_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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    .. note::
        Dynamic graph mode is turn ON by default since paddle 2.0.0

    This API checks whether paddle runs in dynamic graph mode.

    You can turn ON static graph mode by `enable_static <../dygraph/base/disable_dygraph_en.html>`_ ,
    and turn OFF static graph mode by `disable_static <../dygraph/base/enable_dygraph_en.html>`_  .
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    Returns:
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        bool: Whether paddle runs in dynamic graph mode.
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    Examples:
        .. code-block:: python

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            import paddle
            print(paddle.in_dynamic_mode())  # True, dynamic mode is turn ON by default since paddle 2.0.0

            paddle.enable_static()
            print(paddle.in_dynamic_mode())  # False, Now we are in static mode

            paddle.disable_static()
            print(paddle.in_dynamic_mode())  # True, Now we are in dynamic mode
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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 dynamic graph mode, please call 'paddle.disable_static()' to enter dynamic graph mode." % func.__name__
        return func(*args, **kwargs)

    return __impl__


def _static_only_(func):
    def __impl__(*args, **kwargs):
        assert not in_dygraph_mode(
        ), "We only support '%s()' in static graph mode, please call 'paddle.enable_static()' to enter static graph 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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# NOTE(chenweihang): There is argument name typo (stat_dict, correct name is state_dict) 
# in fluid api Layer.set_dict, Optimizer.load, in order to correct the argument without 
# introducing compatibility issues, add this decorator
# NOTE(chenweihang): not using `wrap_decorator` here is because `wrap_decorator` will
# move kwargs to args, which doesn't work in this decorate case
def deprecate_stat_dict(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        if 'stat_dict' in kwargs:
            warnings.warn(
                "The argument `stat_dict` has deprecated, please change it to `state_dict`.",
                DeprecationWarning)
            kwargs['state_dict'] = kwargs['stat_dict']
            kwargs.pop('stat_dict')
        return func(*args, **kwargs)

    return wrapper


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dygraph_not_support = wrap_decorator(_dygraph_not_support_)
dygraph_only = wrap_decorator(_dygraph_only_)
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static_only = wrap_decorator(_static_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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    global _global_expected_place_
    if _global_expected_place_ is None:
        if core.is_compiled_with_cuda():
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            try:
                device_count = core.get_cuda_device_count()
            except Exception as e:
                device_count = 0
            if device_count > 0:
                _global_expected_place_ = core.CUDAPlace(0)
            else:
                warnings.warn(
                    "You are using GPU version Paddle, but your CUDA device is not set properly. CPU device will be used by default."
                )
                _global_expected_place_ = core.CPUPlace()
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        else:
            _global_expected_place_ = core.CPUPlace()

    return _global_expected_place_


def _set_dygraph_tracer_expected_place(place):
    global _dygraph_tracer_
    if _dygraph_tracer_ is not None:
        _dygraph_tracer_._expected_place = place


def _set_expected_place(place):
    global _global_expected_place_
    _global_expected_place_ = place
    _set_dygraph_tracer_expected_place(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_xpu():
    """
    Whether this whl package can be used to run the model on XPU.

    Returns (bool): support xpu or not.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            support_xpu = fluid.is_compiled_with_xpu()
    """
    return core.is_compiled_with_xpu()


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def is_compiled_with_cuda():
    """
    Whether this whl package can be used to run the model on GPU.

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    Returns (bool): `True` if CUDA is currently available, otherwise `False`.
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    Examples:
        .. code-block:: python

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            import paddle
            support_gpu = paddle.is_compiled_with_cuda()
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    """
    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.

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    This function creates a list of :code:`paddle.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
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    be [paddle.CUDAPlace(0), paddle.CUDAPlace(1), paddle.CUDAPlace(2)].
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    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 
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    [paddle.CUDAPlace(0), paddle.CUDAPlace(1), paddle.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 paddle.CUDAPlace: Created GPU place list.
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    Examples:
        .. code-block:: python

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            import paddle
            import paddle.static as static
            
            paddle.enable_static()

            cuda_places = static.cuda_places()
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    """
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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:`paddle.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 paddle.CPUPlace: Created list of CPU places.
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    Examples:
        .. code-block:: python

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            import paddle
            import paddle.static as static
            
            paddle.enable_static()

            cpu_places = static.cpu_places()
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    """

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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):
    """
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    :api_attr: Static Graph

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    Generate hierarchical name prefix for the operators in Static Graph.
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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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        Don't use it in dygraph, since it will cause memory leak.
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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
          paddle.enable_static()
          with paddle.static.name_scope("s1"):
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             a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
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             b = a + 1
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             with paddle.static.name_scope("s2"):
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                c = b * 1
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             with paddle.static.name_scope("s3"):
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                d = c / 1
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          with paddle.static.name_scope("s1"):
                f = paddle.tensor.pow(d, 2.0)
          with paddle.static.name_scope("s4"):
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                g = f - 1

          # Op are created in the default main program.  
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          for op in paddle.static.default_main_program().block(0).ops:
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              # 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
626
    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
638
    elif dtype == np.uint16:
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        # since there is still no support for bfloat16 in NumPy,
        # uint16 is used for casting bfloat16
        return core.VarDesc.VarType.BF16
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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 = []
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    target_block = default_main_program().current_block()
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    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
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            })
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        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):
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                temp_1 = var.block.create_var(dtype=slice_item.dtype)
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                fill_constant([1], 1, force_cpu=True, out=temp_1)
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                temp_end = target_block.create_var(dtype=slice_item.dtype)
800
                target_block.append_op(
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                    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
883
        slice_out_var = target_block.create_var(
884 885 886
            name=unique_name.generate_with_ignorable_key(var.name + "_slice"),
            dtype=var.dtype)

887
        target_block.append_op(
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            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:
895
        strided_slice_out_var = target_block.create_var(
896 897 898
            name=unique_name.generate_with_ignorable_key(var.name +
                                                         "_strided_slice"),
            dtype=var.dtype)
899
        target_block.append_op(
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            type="strided_slice",
            inputs=inputs,
            outputs={'Out': [strided_slice_out_var]},
            attrs=attrs)

        out = strided_slice_out_var

    if len(reverse_axis) > 0:
908
        reverse_out_var = target_block.create_var(
909 910 911
            name=unique_name.generate_with_ignorable_key(var.name +
                                                         "_slice_reverse"),
            dtype=var.dtype)
912
        target_block.append_op(
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            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):
925
    """
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    **Notes**:
927
        **The constructor of Variable should not be invoked directly.**
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929 930
        **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
934
    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.
937

938
    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.
940

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    Most of a Variable's member variables can be set to be None. It mean
942
    it is not available or will be specified later.
943

944
    Examples:
945 946
        In Static Graph Mode:

947 948
        .. code-block:: python

949
            import paddle.fluid as fluid
950
            cur_program = fluid.Program()
951 952 953 954
            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:
956 957 958 959 960 961 962 963 964

        .. 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))

965 966
    """

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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:
987
            if not isinstance(dtype, core.VarDesc.VarType):
988
                dtype = convert_np_dtype_to_dtype_(dtype)
989

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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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998 999 1000
        if self.desc is None:
            self.desc = self.block.desc.var(cpt.to_bytes(name))
            is_new_var = True
1001

1002 1003 1004 1005 1006 1007 1008
        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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1010
        if shape is not None:
1011
            if is_new_var:
1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052
                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))
1053

1054 1055
        if need_check_feed and is_new_var:
            self.desc.set_need_check_feed(need_check_feed)
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1057 1058 1059 1060 1061 1062 1063
        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
1064

1065 1066 1067 1068
        self.block.vars[name] = self
        self.op = None
        self._stop_gradient = stop_gradient
        self.is_data = is_data
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1070
    @fake_interface_only
1071 1072
    def detach(self):
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
1075

1076
        Returns a new Variable, detached from the current graph.
1077

1078
        Returns:
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             ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
1080

1081

1082 1083 1084 1085 1086
        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
1087
                from paddle.fluid.dygraph import Linear
1088 1089 1090 1091
                import numpy as np

                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                with fluid.dygraph.guard():
1092
                    linear = Linear(32, 64)
1093
                    data = to_variable(data)
1094
                    x = linear(data)
1095 1096 1097
                    y = x.detach()

        """
1098
        pass
1099

1100
    @fake_interface_only
1101
    def numpy(self):
1102
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
1105

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        Returns a numpy array shows the value of current :ref:`api_guide_Variable_en`
1107 1108 1109 1110 1111

        Returns:
            ndarray: The numpy value of current Variable.

        Returns type:
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            ndarray: dtype is same as current Variable
1113 1114 1115 1116 1117 1118

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
1119
                from paddle.fluid.dygraph import Linear
1120 1121 1122 1123
                import numpy as np

                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                with fluid.dygraph.guard():
1124
                    linear = Linear(32, 64)
1125
                    data = to_variable(data)
1126
                    x = linear(data)
1127 1128 1129
                    print(x.numpy())

        """
1130
        pass
1131

1132
    @fake_interface_only
1133 1134
    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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1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
        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
1148
                from paddle.fluid.dygraph import Linear
1149 1150
                import numpy as np

1151
                data = np.ones([3, 1024], dtype='float32')
1152
                with fluid.dygraph.guard():
1153
                    linear = fluid.dygraph.Linear(1024, 4)
1154
                    t = to_variable(data)
1155
                    linear(t)  # call with default weight
1156
                    custom_weight = np.random.randn(1024, 4).astype("float32")
1157 1158
                    linear.weight.set_value(custom_weight)  # change existing weight
                    out = linear(t)  # call with different weight
1159 1160

        """
1161
        pass
1162

1163
    @fake_interface_only
1164
    def backward(self, retain_graph=False):
1165
        """
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        **Notes**:
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            **This API is ONLY available in Dygraph mode**
1168

1169
        Run backward of current Graph which starts from current Tensor.
1170

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        Args:
1172 1173 1174 1175
            retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would
                like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter
                :code:`retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient.
                Defaults to False.
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        Returns:
            NoneType: None
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        Examples:
            .. code-block:: python

                import numpy as np
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                import paddle
                paddle.disable_static()
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                x = np.ones([2, 2], np.float32)
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                inputs = []
                for _ in range(10):
                    tmp = paddle.to_tensor(x)
                    # 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.
                    tmp.stop_gradient=False
                    inputs.append(tmp)
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                ret = paddle.add_n(inputs)
                loss = paddle.sum(ret)
1197
                loss.backward()
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        """
1200
        pass
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1202
    @fake_interface_only
1203
    def gradient(self):
1204
        """
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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:
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            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

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                # 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)
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                    loss2.backward()
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                    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())

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        """
1246
        pass
1247

1248
    @fake_interface_only
1249
    def clear_gradient(self):
1250
        """
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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)
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                    loss2.backward()
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                    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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                import paddle
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                paddle.enable_static()
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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)
1752
            index = int(item)
1753
            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):
    """
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    The ComplexTensor defined on the complex number domain. It contains two common 
    real number Tensor as its members, :attr:`real` and :attr:`imag` 
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    holding the real part and imaginary part of complex numbers respectively.
    
    **Notes**:
1786
        **The constructor of ComplexTensor should not be invoked directly.**
1787 1788

    Args:
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        real (Tensor): The Tensor holding real-part data.
        imag (Tensor): The Tensor holding imaginery-part data.
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    Examples:
        .. code-block:: python

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            import paddle
            x = paddle.to_tensor([1.0+2.0j, 0.2])
            print(x.name, x.dtype, x.shape)
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            # ({'real': 'generated_tensor_0.real', 'imag': 'generated_tensor_0.imag'}, complex64, [2])
            print(x)
            # ComplexTensor[real](shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #                     [        1., 0.20000000])
            # ComplexTensor[imag](shape=[2], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
            #                     [2., 0.])
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            print(type(x))
            # <class 'paddle.ComplexTensor'>
1806 1807
    """

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    def __new__(cls, *arg, **kwargs):
        cls.__module__ = "paddle"
        cls.__name__ = "ComplexTensor"
        return super(ComplexVariable, cls).__new__(cls)

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    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

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    def __getitem__(self, idx):
        return ComplexVariable(self.real[idx], self.imag[idx])

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    @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):
1862 1863 1864 1865
        from paddle.tensor.to_string import to_string
        return "ComplexTensor containing:\n{real}\n{imag}".format(
            real=to_string(self.real, "[real part]Tensor"),
            imag=to_string(self.imag, "[imag part]Tensor"))
1866 1867 1868 1869

    __repr__ = __str__


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class OpProtoHolder(object):
1871 1872 1873 1874
    """
    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__,
1884
            '_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):
1891 1892 1893 1894 1895 1896 1897 1898
        """
        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

1909 1910 1911 1912
    @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(),
1915 1916
            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName(),
            core.op_proto_and_checker_maker.kOpDeviceAttrName()
1917 1918
        }

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class Operator(object):
1921
    """
1922 1923 1924 1925 1926 1927 1928
    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.
1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949
        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.
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    Examples:
        .. code-block:: python

1955
            import paddle.fluid as fluid
1956
            cur_program = fluid.Program()
1957 1958 1959 1960 1961
            cur_block = cur_program.current_block()
            # var1 += var2 + var3
            cur_block.append_op(type="sum",
                                inputs={"X": [var1, var2, var3]},
                                outputs={"Out": [var1]})
1962
    """
1963
    OP_WITHOUT_KERNEL_SET = {
1964 1965
        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
1966 1967
        'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
        'gen_nccl_id', 'c_gen_nccl_id', 'c_comm_init', 'c_sync_calc_stream',
1968
        'c_sync_comm_stream', 'queue_generator', 'dequeue', 'enqueue'
1969
    }
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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():
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            if type is None:
                raise ValueError(
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                    "`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 {}
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        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(
1998
                )] = self.block.program._op_role
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            role_var_name = op_maker.kOpRoleVarAttrName()
            if len(self.block.program.
2002 2003
                   _op_role_var) != 0 and role_var_name not in op_attrs:
                op_attrs[role_var_name] = self.block.program._op_role_var
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            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(
2012
                    "`type` to initialized an Operator can not be None.")
2013 2014
            else:
                callstack_var_name = op_maker.kOpCreationCallstackAttrName()
2015 2016 2017 2018 2019 2020 2021
                op_attrs[callstack_var_name] = []
                for frame in traceback.extract_stack():
                    op_attrs[callstack_var_name].append(
                        '  File "{}", line {}, in {}'.format(frame[0], frame[1],
                                                             frame[2]))
                    op_attrs[callstack_var_name].append('    {}'.format(frame[
                        3]))
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            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()

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            # 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)

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            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]
2060
                        if not isinstance(in_args, (list, tuple)):
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                            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 = []
2067
                        for index, arg in enumerate(in_args):
2068 2069 2070 2071
                            if isinstance(arg, six.string_types):
                                in_arg_names.append(arg)
                            elif isinstance(arg, six.binary_type):
                                in_arg_names.append(arg.decode())
2072
                            elif isinstance(arg, (Variable, core.VarBase)):
2073
                                in_arg_names.append(cpt.to_text(arg.name))
2074
                            else:
2075 2076 2077 2078
                                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."
2079 2080
                                    "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:
2105 2106 2107 2108
                        if isinstance(arg, six.string_types):
                            out_arg_names.append(arg)
                        else:
                            out_arg_names.append(cpt.to_text(arg.name))
2109
                        # TODO(minqiyang): could we remove variable's op in static mode?
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                        if not in_dygraph_mode():
2111 2112 2113 2114
                            if isinstance(arg, six.string_types):
                                block.var(arg).op = self
                            else:
                                arg.op = self
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                    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):
2134 2135
        return op_type not in self.OP_WITHOUT_KERNEL_SET

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    def to_string(self, throw_on_error):
2137
        """
2138 2139
        Get debug string.

2140
        Args:
2141 2142
            throw_on_error(bool): Whether to raise exception if self is not
                initialized.
2143

2144 2145
        Returns:
            str: The debug string.
2146 2147

        """
2148
        protostr = self.desc.serialize_to_string()
2149
        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):
2246
        return self._to_readable_code()
2247 2248 2249

    __repr__ = __str__

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    @property
    def type(self):
2252
        return self.desc.type()
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    def input(self, name):
2255
        """
2256
        Get the input arguments according to the input parameter name.
2257

2258 2259
        Args:
            name(str): The input parameter name.
2260

2261 2262 2263
        Returns:
            list: return the list of argument names that associated with \
                the specific parameter name.
2264
        """
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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):
2306
        """
2307
        Get output arguments by the output parameter name.
2308

2309 2310
        Args:
            name(str): The output parameter name.
2311

2312 2313 2314
        Returns:
            list: return the list of argument names associated with \
                the specific parameter name.
2315
        """
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        return self.desc.output(name)

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

2322 2323 2324 2325 2326 2327 2328 2329
    @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):
2331
        """
2332 2333
        Whether this Operator has the attribute with name or not.

2334
        Args:
2335
            name(str): the attribute name.
2336

2337 2338
        Returns:
            bool: True if has this attribute.
2339 2340

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

    def attr_type(self, name):
2344
        """
2345
        Get the type of attribute by attribute's name.
2346

2347 2348
        Args:
            name(str): the attribute name.
2349

2350 2351
        Returns:
            core.AttrType: the attribute type.
2352
        """
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        return self.desc.attr_type(name)

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    def _set_attr(self, name, val):
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        """
        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)

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    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):
2386
            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):
2398
        """
2399 2400
        Get the attribute by name.

2401
        Args:
2402
            name(str): the attribute name.
2403

2404 2405
        Returns:
            bool|int|str|float|list: The attribute value. The return value
2406 2407
            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):
2411
        """
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        Get the block attribute's id by name.
2413

2414 2415
        Args:
            name(str): the attribute name.
2416

2417 2418
        Returns:
            int: the block index.
2419
        """
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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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        """
2469 2470 2471
        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

2490 2491 2492
    def _is_optimize_op(self):
        op_maker = core.op_proto_and_checker_maker
        OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize
2493 2494 2495 2496

        if not self.desc.has_attr(op_maker.kOpRoleAttrName()):
            return False

2497 2498 2499
        op_role = self.desc.attr(op_maker.kOpRoleAttrName())
        if op_role & int(OPTIMIZE):
            return True
2500 2501 2502 2503 2504 2505 2506 2507

        return False

    def _is_backward_op(self):
        op_maker = core.op_proto_and_checker_maker
        BACKWARD = core.op_proto_and_checker_maker.OpRole.Backward

        if not self.desc.has_attr(op_maker.kOpRoleAttrName()):
2508 2509
            return False

2510 2511 2512 2513 2514 2515
        op_role = self.desc.attr(op_maker.kOpRoleAttrName())
        if op_role & int(BACKWARD):
            return True

        return False

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class Block(object):
2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531
    """
    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.
2533 2534 2535 2536

    Examples:
        .. code-block:: python

2537 2538 2539
            import paddle.fluid as fluid

            cur_program = fluid.Program()
2540 2541 2542 2543 2544 2545 2546 2547 2548
            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)
2551
        self.vars = collections.OrderedDict()  # var_name --> var
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        self.ops = list()  # operator list
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        self.program = program
2554
        self.removed_vars = collections.OrderedDict()
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2556
    def __str__(self):
2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602
        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):
        """
2606 2607
        Get debug string.

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        Args:
            throw_on_error(bool): raise exception when self is not initialized
2610
                when throw_on_error is True.
F
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            with_details(bool): more details about variables and parameters
2612 2613
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False.
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2615 2616
        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)
2624
            for var in list(self.vars.values()):
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                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
F
update  
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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()
2633 2634
            proto = framework_pb2.BlockDesc.FromString(
                six.binary_type(protostr))
F
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
2637 2638 2639

    __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):
2649 2650 2651 2652 2653 2654 2655 2656 2657
        """
        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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2660 2661 2662 2663 2664 2665 2666 2667
    @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):
2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685
        """
        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.
        """
2686
        if not isinstance(name, six.string_types):
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2687 2688 2689
            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:
Q
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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):
2696 2697 2698 2699 2700 2701 2702
        """
        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.
2704
        """
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2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728
        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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2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749
    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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Q
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    def all_parameters(self):
2752
        return list(self.iter_parameters())
2753

2754
    def iter_parameters(self):
M
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2755
        return (item[1] for item in six.iteritems(self.vars)
2756
                if isinstance(item[1], Parameter))
Q
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2757

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    def create_var(self, *args, **kwargs):
L
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2759 2760 2761
        if in_dygraph_mode():
            var = _varbase_creator(*args, **kwargs)
        else:
2762 2763 2764
            var = Variable(block=self, *args, **kwargs)
            if 'initializer' in kwargs:
                kwargs['initializer'](var, self)
Q
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        return var
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Q
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2767 2768 2769
    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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2771 2772
        """
        Rename variable in vars and ops' inputs and outputs
2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784

        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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        """
M
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2786 2787
        name = cpt.to_text(name)
        new_name = cpt.to_text(new_name)
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2788

T
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2789
        if not self.has_var(name):
2790
            raise ValueError("var %s is not in current block" % name)
T
wip  
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2791 2792
        v = self.var(name)
        if type(v) == Parameter:
T
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2793
            var_type = "Parameter"
T
wip  
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2794 2795 2796 2797 2798 2799
            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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2800
            var_type = "Variable"
T
wip  
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2801 2802 2803 2804
            error_clip = v.error_clip
            stop_gradient = v.stop_gradient
        else:
            raise ValueError("unsupported var type: %s", type(v))
T
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2805
        orig_var_type = v.type
M
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2806
        self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
W
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2807
        # NOTE: v is destroyed by C++ after calling _rename_var.
M
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2808
        d = self.desc.find_var(cpt.to_bytes(new_name))
T
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2809
        if var_type == "Parameter":
L
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2810 2811
            if in_dygraph_mode():
                var = ParamBase(
2812 2813 2814 2815 2816 2817 2818 2819 2820 2821
                    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:
L
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2822 2823
                var = Parameter(
                    self,
2824 2825 2826 2827 2828 2829 2830 2831 2832
                    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)
T
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        elif var_type == "Variable":
T
wip  
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2834 2835
            var = Variable(
                self,
T
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2836
                type=orig_var_type,
T
wip  
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2837 2838 2839 2840
                name=new_name,
                error_clip=error_clip,
                stop_gradient=stop_gradient)

W
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2841
        # rename the python side, _sync_with_cpp will only add
T
wip  
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2842 2843 2844
        # new vars/ops to python side.
        self.vars[new_name] = var
        del self.vars[name]
W
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2845
        self._sync_with_cpp()
2846
        return var
T
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2848 2849 2850
    def _remove_var(self, name, sync=True):
        if sync == True:
            self._sync_with_cpp()
M
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2851
        self.desc._remove_var(cpt.to_bytes(name))
2852 2853
        del self.vars[name]

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2854 2855
    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
2856
        param = None
L
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2857
        if in_dygraph_mode():
2858
            param = ParamBase(*args, **kwargs)
L
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2859 2860
        else:
            param = Parameter(global_block, *args, **kwargs)
2861 2862 2863 2864 2865 2866
            # NOTE: Why only set stop_gradient=False in static mode
            # Because in dygraph mode, the `stop_gradient` and `trainable`
            # are related, and `trainable` default vallue is `True` or
            # it is specified by users, there is no need to set
            # `stop_gradient` for ParamBase here.
            param.stop_gradient = False
2867
        if 'initializer' in kwargs:
2868 2869 2870 2871 2872

            def _is_inited_by(block, var):
                init_ops = []
                for op in block.ops:
                    if var.name in op.output_arg_names:
2873 2874 2875 2876 2877
                        # 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
2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888
                        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:
2889
                # TODO already inited, do nothing, should log a warning
2890 2891 2892
                pass
            else:
                initializer(param, self)
Q
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        return param
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    def append_op(self, *args, **kwargs):
2896 2897 2898 2899 2900 2901
        """
        Appends a new Operator according to the giving arguments.

        Returns:
            Operator: the append Operator.
        """
L
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        if in_dygraph_mode():
2903
            attrs = kwargs.get("attrs", {})
J
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2904
            type = kwargs.get("type", None)
2905 2906 2907
            op = Operator(
                block=self,
                desc=None,
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                type=type,
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2909 2910
                inputs=None,
                outputs=None,
2911
                attrs=attrs)
2912

M
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2913 2914 2915
            # record ops in tracer rather than blocks
            #
            # TODO(minqiyang): add op stop_gradient support in static mode too.
L
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2916
            # currently, we only support stop_gradient in dygraph mode.
J
Jiabin Yang 已提交
2917 2918

            _dygraph_tracer().trace_op(type,
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2919
                                       kwargs.get("inputs", {}),
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2920 2921
                                       kwargs.get("outputs", {}), attrs
                                       if attrs else {},
M
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                                       kwargs.get("stop_gradient", False))
M
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        else:
2924 2925 2926 2927 2928 2929 2930 2931 2932
            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))

M
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            self.ops.append(op)
M
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2935 2936
        return op

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2937
    def _insert_op(self, index, *args, **kwargs):
2938 2939 2940 2941 2942 2943 2944 2945 2946
        """
        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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2947 2948
        self._sync_with_cpp()
        op_desc = self.desc._insert_op(index)
Q
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2949 2950 2951 2952
        op = Operator(block=self, desc=op_desc, *args, **kwargs)
        self.ops.insert(index, op)
        return op

2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969
    def _insert_op_without_sync(self, index, *args, **kwargs):
        """
        Insert an Operator according to the giving arguments, 
        without sync_with_cpp to meke the compilation faster.

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

        Returns:
            Operator: the insert Operator.
        """
        op_desc = self.desc._insert_op(index)
        op = Operator(block=self, desc=op_desc, *args, **kwargs)
        self.ops.insert(index, op)
        return op

    def _remove_op(self, index, sync=True):
2970 2971 2972 2973 2974 2975 2976 2977 2978
        """
        Remove the specific position operator.

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

        Returns:
            None
        """
2979 2980
        if sync == True:
            self._sync_with_cpp()
W
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2981
        self.desc._remove_op(index, index + 1)
2982 2983
        del self.ops[index]

W
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2984
    def _slice_ops(self, start, end):
2985 2986 2987 2988 2989 2990 2991 2992 2993 2994
        """
        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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        return self.ops[start:end]
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W
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2997
    def _prepend_op(self, *args, **kwargs):
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        if in_dygraph_mode():
J
Jiabin Yang 已提交
2999 3000
            type = kwargs.get("type", None)
            attrs = kwargs.get("attrs", {})
3001
            op = Operator(
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3002
                self, None, type=type, inputs=None, outputs=None, attrs=attrs)
M
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J
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3004
            _dygraph_tracer().trace_op(type,
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                                       kwargs.get("inputs", {}),
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3006 3007
                                       kwargs.get("outputs", {}), attrs
                                       if attrs else {},
M
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                                       kwargs.get("stop_gradient", False))
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3009
        else:
3010 3011 3012 3013 3014 3015 3016 3017
            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))
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            self.ops.insert(0, op)
3019

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3020 3021
        return op

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3022
    def _sync_with_cpp(self):
3023
        """
3024 3025
        Sync from the desc on the c++ end. This method is used to synchronize
        the c++ desc instance generated by backward.
3026
        """
Q
Qiao Longfei 已提交
3027 3028 3029 3030 3031
        # 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())

3032
        # sync variables removed from c++ end
3033
        for var in list(self.vars.keys()):
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minqiyang 已提交
3034
            if not self.desc.find_var(cpt.to_bytes(var)):
3035 3036
                self.vars.pop(var)

Q
Qiao Longfei 已提交
3037
        # sync operators from cpp
3038 3039 3040 3041
        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

Y
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3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057
        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
3058 3059 3060 3061 3062

        # 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
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            self.ops.insert(0, op)
Q
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3064 3065 3066 3067 3068 3069 3070

        # 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)

3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083
        # 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

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3084 3085 3086 3087
        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
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3088
    def _copy_param_info_from(self, other):
3089
        """
3090 3091
        Copy the information of parameters from the other block.

3092
        Args:
3093 3094 3095 3096 3097
            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.
3098 3099 3100 3101 3102

        Returns:
            None
        """
        if not isinstance(other, Block):
W
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3103 3104
            raise TypeError(
                "_copy_param_info_from should be invoked with Block")
3105
        for p in other.iter_parameters():
3106 3107 3108
            assert isinstance(p, Parameter)
            v = self.vars.get(p.name, None)
            if v is None:
3109 3110
                # if the Parameter is pruned, v may be None
                continue
3111
            assert isinstance(v, Variable)
3112
            new_p = None
L
Leo Chen 已提交
3113 3114
            if in_dygraph_mode():
                new_p = ParamBase(
3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125
                    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:
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3126 3127
                new_p = Parameter(
                    block=self,
3128 3129 3130
                    shape=v.shape,
                    dtype=v.dtype,
                    type=v.type,
3131 3132
                    lod_level=v.lod_level
                    if v.type == core.VarDesc.VarType.LOD_TENSOR else None,
3133 3134 3135 3136 3137 3138
                    stop_gradient=p.stop_gradient,
                    trainable=p.trainable,
                    optimize_attr=p.optimize_attr,
                    regularizer=p.regularizer,
                    error_clip=p.error_clip,
                    name=v.name)
3139 3140
            self.vars[new_p.name] = new_p

3141
    def _clone_variable(self, var, force_persistable=True):
3142 3143
        """
        Clone a variable into current block.
3144

3145 3146
        Args:
            var: the variable to be cloned.
3147 3148 3149
            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.
3150 3151

        Returns:
3152
            Variable: the new  variable cloned from 'var' in current block.
3153 3154
        """
        assert isinstance(var, Variable)
T
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typhoonzero 已提交
3155 3156 3157 3158 3159
        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)
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3160 3161
        elif var.type == core.VarDesc.VarType.RAW:
            ret_var = self.create_var(
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3162
                name=var.name, persistable=var.persistable, type=var.type)
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3163 3164 3165 3166 3167 3168
        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,
3169
                persistable=True if force_persistable else var.persistable,
H
Huihuang Zheng 已提交
3170 3171
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
typhoonzero 已提交
3172 3173 3174 3175 3176 3177 3178
        else:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
3179
                persistable=True if force_persistable else var.persistable,
H
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3180 3181
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
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3182
        return ret_var
3183

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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 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279
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()

3280
    def remove_input_by_id(self, node_id):
3281 3282 3283 3284 3285 3286
        """
        Remove a node from inputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
3287
        self.node.remove_input(node_id)
3288

3289
    def remove_input(self, node):
3290 3291 3292 3293
        """
        Remove a node from inputs.

        Args:
3294
            node(IrNode): the node being removed.
3295
        """
3296
        self.node.remove_input(node.node)
3297

3298
    def append_input(self, node):
3299 3300 3301 3302
        """
        Append a node in inputs.

        Args:
3303
            node(IrNode): the node being appended.
3304
        """
3305
        self.node.append_input(node.node)
3306 3307 3308 3309 3310 3311 3312 3313

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

3314
    def remove_output_by_id(self, node_id):
3315 3316 3317 3318 3319 3320
        """
        Remove a node from outputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
3321
        self.node.remove_output(node_id)
3322

3323
    def remove_output(self, node):
3324 3325 3326 3327
        """
        Remove a node from outputs.

        Args:
3328
            node(IrNode): the node being removed.
3329
        """
3330
        self.node.remove_output(node.node)
3331

3332
    def append_output(self, node):
3333 3334 3335 3336
        """
        Append a node in outputs.

        Args:
3337
            node(IrNode): the node being appended.
3338
        """
3339
        self.node.append_output(node.node)
3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386

    @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
3387
            "The node variable description can not be None."
3388 3389 3390 3391 3392 3393 3394 3395 3396 3397
        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
3398
            "The node variable description can not be None."
3399 3400
        return self.node.var().persistable()

3401 3402 3403 3404 3405 3406 3407 3408
    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
3409
            "The node variable description can not be None."
3410 3411 3412 3413 3414 3415 3416 3417 3418 3419
        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
3420
            "The node variable description can not be None."
3421 3422 3423 3424 3425 3426 3427 3428 3429 3430
        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
3431
            "The node variable description can not be None."
3432 3433
        return self.node.var().shape()

3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480
    @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
3481
            "The node operator description can not be None."
3482 3483
        self.node.op()._rename_input(old_input_name, new_input_name)

3484 3485 3486 3487 3488 3489 3490 3491 3492
    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
3493
            "The node operator description can not be None."
3494 3495
        self.node.op()._rename_output(old_output_name, new_output_name)

3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506
    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
3507
            "The node operator description can not be None."
3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520
        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
3521
            "The node operator description can not be None."
3522 3523 3524 3525 3526 3527 3528 3529 3530 3531
        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
3532
            "The node operator description can not be None."
3533 3534
        return self.node.op().set_type(new_type)

3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549
    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
3550
            "The node operator description can not be None."
3551 3552 3553 3554
        desc = self.node.op()
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and \
3555
                all(isinstance(v, Block) for v in val):
3556 3557
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
3558
                isinstance(val, core.ProgramDesc):
3559 3560 3561 3562
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)

3563 3564 3565 3566 3567 3568 3569 3570
    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
3571
            "The node operator description can not be None."
3572 3573 3574 3575 3576 3577 3578 3579 3580 3581
        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
3582
            "The node operator description can not be None."
3583 3584
        return self.node.op().output_arg_names()

3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605
    @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]


3606 3607
class IrGraph(object):
    """
3608
    Python IrGraph. Beneath it is a core.Graph, which is used for
3609
    creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
3610 3611
    a Program. In an IrGraph, both Variables and Operators are graph
    nodes.
3612 3613 3614 3615
    """

    def __init__(self, graph, for_test=False):
        """
3616 3617
        Construct an IrGraph using core.Graph.

3618 3619 3620 3621 3622 3623 3624 3625 3626
        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

3627 3628 3629 3630
    def clone(self):
        """
        Create a new and duplicated IrGraph.

3631 3632 3633
        Warns:
            The method only clones the graph structure, not its attributes.

3634 3635 3636
        Returns:
            IrGraph: A new and duplicated graph.
        """
3637
        g = self.graph.clone()
3638 3639
        return IrGraph(g, self._for_test)

3640
    def is_test(self):
3641 3642 3643
        """
        If the graph is used for testing, the function returns true. Otherwise, returns false.
        """
3644 3645
        return self._for_test

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3646
    def all_nodes(self):
3647 3648 3649
        """
        Return all nodes included in the graph as a set.
        """
3650
        return {IrNode(node) for node in self.graph.nodes()}
3651

3652
    def all_var_nodes(self):
3653 3654 3655
        """
        Return all variable nodes included in the graph as a set.
        """
3656
        return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
3657

3658
    def all_persistable_nodes(self):
3659 3660 3661
        """
        Return all persistable variable nodes included in the graph as a set.
        """
W
WangZhen 已提交
3662 3663 3664 3665 3666
        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)
3667
        return {IrVarNode(p) for p in persistable_nodes}
W
WangZhen 已提交
3668

3669
    def all_op_nodes(self):
3670 3671 3672
        """
        Return all operator nodes included in the graph as a set.
        """
3673
        return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
3674

3675
    def create_persistable_node(self, name, var_type, shape, var_dtype):
3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686
        """
        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:
3687
            IrVarNode: the created persistable variable node.
3688
        """
3689 3690 3691 3692 3693
        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)
3694
        return IrVarNode(self.graph.create_var_node(var_desc))
3695 3696

    def create_var_node(self, name, var_type, shape, var_dtype):
3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707
        """
        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:
3708
            IrVarNode: the created variable node.
3709 3710
        """

3711 3712 3713 3714
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
3715
        return IrVarNode(self.graph.create_var_node(var_desc))
3716

3717 3718 3719 3720 3721 3722
    def create_control_dep_var(self):
        """
        create a control var
        """
        return IrVarNode(self.graph.create_control_dep_var())

3723
    def create_var_node_from_desc(self, var_desc):
3724 3725 3726 3727 3728 3729 3730 3731
        """
        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:
3732
            IrVarNode: the created variable node.
3733
        """
3734
        return IrVarNode(self.graph.create_var_node(var_desc))
3735 3736

    def create_op_node(self, op_type, attrs, inputs, outputs):
3737 3738 3739 3740 3741 3742 3743
        """
        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.
T
tianshuo78520a 已提交
3744
            outputs(dict): the outputs of the operator node.
3745 3746

        Returns:
3747
            IrOpNode: the created operator node.
3748
        """
3749 3750
        op_desc = core.OpDesc()
        op_desc.set_type(op_type)
3751
        for attr, value in six.iteritems(attrs):
3752
            self._update_desc_attr(op_desc, attr, value)
3753
        for input_name, var_nodes in six.iteritems(inputs):
3754 3755 3756 3757
            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])
3758
        for output_name, var_nodes in six.iteritems(outputs):
3759 3760 3761 3762
            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])
3763
        return IrOpNode(self.graph.create_op_node(op_desc))
3764 3765

    def create_op_node_from_desc(self, op_desc):
3766 3767 3768 3769 3770 3771 3772
        """
        Create a operator node by using an existing OpDesc in the graph.

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

        Returns:
3773
            IrOpNode: the created operator node.
3774
        """
3775
        return IrOpNode(self.graph.create_op_node(op_desc))
3776 3777

    def update_input_link(self, old_input_node, new_input_node, op_node):
3778 3779 3780 3781
        """
        Update the input's link of a operator node.

        Args:
3782 3783 3784
            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.
3785
        """
3786
        assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
3787 3788
               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.'
3789 3790 3791 3792
        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)
3793
        op_node.rename_input(old_input_node.name(), new_input_node.name())
3794

3795 3796 3797 3798 3799 3800 3801 3802 3803 3804
    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 \
3805 3806
               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.'
3807 3808 3809 3810 3811 3812
        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())

3813
    def link_to(self, node_in, node_out):
3814 3815 3816 3817
        """
        Connect two nodes.

        Args:
3818 3819
            node_in(IrNode): the input node.
            node_out(IrNode): the output node.
3820
        """
3821
        assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \
W
WangZhen 已提交
3822
            'The two arguments(node_in&node_out) must be in the graph nodes.'
3823 3824
        node_in.append_output(node_out)
        node_out.append_input(node_in)
3825 3826

    def safe_remove_nodes(self, remove_nodes):
3827 3828 3829 3830 3831 3832 3833
        """
        Remove nodes safely since links connected to these removed nodes are
        also removed.

        Args:
            remove_nodes(set): the nodes prepared to be removed.
        """
3834
        if not isinstance(remove_nodes, set):
W
WangZhen 已提交
3835 3836 3837 3838
            if isinstance(remove_nodes, Iterable):
                remove_nodes = set(remove_nodes)
            else:
                remove_nodes = {remove_nodes}
3839 3840
        original_nodes = {n.node for n in remove_nodes}
        core.graph_safe_remove_nodes(self.graph, original_nodes)
3841

Z
Zhen Wang 已提交
3842 3843 3844 3845 3846 3847 3848 3849
    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] = [
3850
                            self._find_node_by_name(node.inputs, each_var_name)
Z
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3851 3852 3853 3854
                        ]
                for each_var_name in node.op().output_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
3855
                            self._find_node_by_name(node.outputs, each_var_name)
Z
Zhen Wang 已提交
3856 3857 3858
                        ]
                    else:
                        var_nodes[each_var_name].append(
3859 3860
                            self._find_node_by_name(node.outputs,
                                                    each_var_name))
Z
Zhen Wang 已提交
3861 3862
        self.graph.resolve_hazard(var_nodes)

W
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3863
    def has_circle(self):
3864 3865 3866 3867 3868 3869
        """
        Check if the graph has a circle.

        Returns:
            bool: True if the graph has a circle else False.
        """
W
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3870 3871 3872
        return core.has_circle(self.graph)

    def graph_num(self):
3873 3874 3875 3876 3877 3878
        """
        Count the number of unconnected graphs in this graph.

        Returns:
            int: the number of unconnected graphs.
        """
W
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3879 3880 3881
        return core.graph_num(self.graph)

    def topology_sort(self):
3882 3883 3884
        """
        Perform the topology sort operation on the graph.

T
tianshuo78520a 已提交
3885
        Notes: the `graph` can not contain a circle.
3886 3887

        Returns:
Z
Zhen Wang 已提交
3888
            list(IrNode): nodes in topology order.
3889
        """
3890
        ordered_nodes = core.topology_sort(self.graph)
Z
Zhen Wang 已提交
3891
        return [IrNode(n) for n in ordered_nodes]
W
WangZhen 已提交
3892 3893

    def build_adjacency_list(self):
3894 3895 3896 3897
        """
        Build an adjacency list of operations for the `graph`.

        Returns:
3898
            dict{IrNode: set(IrNode)}: the adjacency list.
3899
        """
3900 3901 3902 3903 3904
        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
WangZhen 已提交
3905

3906 3907 3908 3909 3910 3911 3912 3913
    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.
3914
            marked_nodes(set(IrNode)): nodes that are needed to be marked.
3915 3916 3917 3918 3919
            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.
        """

3920 3921 3922
        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 \
3923
                                          + ' -o ' + pdf_save_path, shell=True)
3924 3925 3926 3927 3928
            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))

3929
        remove_ctr_vars = set()
3930
        if remove_ctr_var:
3931
            for node in self.all_var_nodes():
3932 3933 3934
                if node.is_ctrl_var():
                    remove_ctr_vars.add(node)
            self.safe_remove_nodes(remove_ctr_vars)
3935 3936
        print('Total ops num = {}.'.format(len(self.all_op_nodes())))

3937 3938
        if marked_nodes is not None:
            if not isinstance(marked_nodes, set):
3939 3940 3941 3942 3943 3944
                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}
3945 3946 3947 3948
            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)
3949 3950
        if not os.path.exists(save_path):
            os.makedirs(save_path)
3951 3952 3953 3954 3955 3956 3957
        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):
3958 3959 3960
        """
        Convert the graph into a Program.

Z
Zhen Wang 已提交
3961
        WARN: When the graph includes backward operator nodes, the
3962 3963 3964 3965 3966 3967
        conversion process may be failed. Usually, this function is
        only used to convert a test graph.

        Returns:
            Program: a program converted from the graph.
        """
3968
        convert_pass = core.get_pass('graph_to_program_pass')
3969 3970
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
3971 3972 3973 3974
        convert_pass.apply(self.graph)
        program = Program._construct_from_desc(desc)
        return program

3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985
    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

3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001
    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)


Y
Yu Yang 已提交
4002
class Program(object):
D
dzhwinter 已提交
4003
    """
4004
    Create Python Program.  It has at least one :ref:`api_guide_Block_en`, when the
4005
    control flow op like conditional_block, while :ref:`api_paddle_fluid_layers_While` is included,
J
Jiabin Yang 已提交
4006
    it will contain nested block.
4007

J
Jiabin Yang 已提交
4008 4009 4010
    Please reference the
    `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_
    for details.
D
dzhwinter 已提交
4011

J
Jiabin Yang 已提交
4012
    A set of Program usually contains startup program and main program.
J
Jiabin Yang 已提交
4013
    A startup program is set to contain some initial work, eg. initialize the ``Parameter``, and the main
J
Jiabin Yang 已提交
4014 4015 4016 4017 4018 4019 4020
    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
Jiabin Yang 已提交
4021
    **Notes**:
4022 4023 4024
        **we have** :ref:`api_paddle_fluid_framework_default_startup_program` **and** :ref:`api_paddle_fluid_framework_default_main_program`
        **by default, a pair of them will shared the parameters. The** :ref:`api_paddle_fluid_framework_default_startup_program` **only run once to initialize parameters,**
        :ref:`api_paddle_fluid_framework_default_main_program` **run in every mini batch and adjust the weights.**
D
dzhwinter 已提交
4025 4026

    Returns:
J
Jiabin Yang 已提交
4027
        Program: An empty Program.
D
dzhwinter 已提交
4028 4029

    Examples:
4030 4031
        .. code-block:: python

4032 4033 4034 4035
            import paddle
            import paddle.static as static

            paddle.enable_static()
4036

4037 4038 4039 4040 4041
            main_program = static.Program()
            startup_program = static.Program()
            with static.program_guard(main_program=main_program, startup_program=startup_program):
                x = static.data(name="x", shape=[-1, 784], dtype='float32')
                y = static.data(name="y", shape=[-1, 1], dtype='int32')
4042
                z = static.nn.fc(name="fc", x=x, size=10, activation="relu")
4043 4044 4045

            print("main program is: {}".format(main_program))
            print("start up program is: {}".format(startup_program))
D
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4046 4047 4048

    """

4049 4050
    def __init__(self):
        self.desc = core.ProgramDesc()
Y
Yu Yang 已提交
4051 4052
        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0
4053 4054
        global global_prog_seed
        self._seed = global_prog_seed
Y
yuyang18 已提交
4055
        self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
4056
        self.__op_role_var = []
T
tangwei12 已提交
4057

4058 4059
        # for distribute training
        # _is_distributed = True if under distributed training
T
tangwei12 已提交
4060
        self._is_distributed = False
4061
        # _is_chief = True if the trainer is the first one, usually No.0
T
tangwei12 已提交
4062
        self._is_chief = False
4063 4064 4065
        # _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
4066
        self._endpoints = []
4067 4068 4069
        # if current role is parameter server, the _ps_endpoint is its "ip:port"
        self._ps_endpoint = None
        # trainers_endpoints, it is used for distribution.
4070
        self._trainers_endpoints = []
4071
        # the distributed lookup table names
T
tangwei12 已提交
4072
        self._distributed_lookup_table = None
4073 4074 4075

        # use Deep gradient comrepssion or not
        self._enable_dgc = False
4076 4077
        self._use_lamb = False

4078 4079 4080
        self._nccl_comm_num = 1
        self._use_hierarchical_allreduce = False
        self._hierarchical_allreduce_inter_nranks = 0
4081

4082 4083 4084
        # 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
4085
        self._program_config = None
4086

H
hutuxian 已提交
4087 4088 4089
        # assigned if this program has been parsed by a pipeline optimizer
        self._pipeline_opt = None

4090 4091 4092
        # appending gradients times
        self._appending_grad_times = 0

4093 4094 4095 4096
        # identifier for auto checkpoint
        self._auto_checkpoint_name = unique_name.generate(
            "__auto_checkpoint_program__")

4097 4098 4099
        # compiled program, i.e. Graph
        self._graph = None

4100 4101 4102 4103 4104 4105 4106 4107 4108 4109
    def global_seed(self, seed=0):
        """
        Set global seed for Program

        Returns:
            None.

        Examples:
            .. code-block:: python

4110 4111
                import paddle
                import paddle.static as static
4112

4113 4114 4115
                paddle.enable_static()

                prog = static.default_main_program()
4116 4117 4118 4119 4120
                print(prog.random_seed)
                ## 0
                ## the default random seed is 0

                prog.global_seed(102)
4121
                prog1 = static.default_main_program()
4122 4123 4124 4125 4126 4127 4128 4129
                print(prog1.random_seed)
                ## 102
                ## the random seed is 102
        """
        global global_prog_seed
        global_prog_seed = seed
        self._seed = global_prog_seed

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    @property
4131
    def _op_role(self):
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        """
        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
4140
        parameter gradient of backward (use :code:`_op_role_var` to get this
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        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.
        """
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        return self._current_role

4147 4148
    @_op_role.setter
    def _op_role(self, role):
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        self._current_role = role

    @property
4152
    def _op_role_var(self):
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        """
4154
        The auxiliary variables for :code:`_op_role` property.
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4155

4156
        See Also: :code:`Program._op_role`'s documentation for details.
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        Notes: This is a very low-level API. Users should not use it directly.
        """
4160
        return self.__op_role_var
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4162
    @signature_safe_contextmanager
4163 4164 4165 4166 4167
    def _backward_role_guard(self):
        tmp_role = self._current_role

        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Backward
4168 4169 4170 4171
        try:
            yield
        finally:
            self._current_role = tmp_role
4172

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4173
    @signature_safe_contextmanager
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    def _optimized_guard(self, param_and_grads):
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        """
        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:
4182
            param_and_grads(list): The variables (names) to be optimized.
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        Examples:

4186
            >>> import paddle.fluid as fluid
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            >>> p, g = backward(...)
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            >>> with program._optimized_guard([p,g]):
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            >>>     p = p - 0.001 * g
        """
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        tmp_role = self._current_role
4192
        tmp_var = self.__op_role_var
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        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Optimize
4196
        self.__op_role_var = [
4197 4198 4199
            var.name if isinstance(var, Variable) else var
            for var in param_and_grads
        ]
4200 4201 4202 4203 4204
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
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4205

S
rename  
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4206
    @signature_safe_contextmanager
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    def _lr_schedule_guard(self, is_with_opt=False):
4208 4209 4210 4211 4212 4213 4214
        """
        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.

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        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.
4219 4220 4221

        Examples:

4222
            >>> import paddle.fluid as fluid
4223 4224 4225 4226
            >>> p, g = backward(...)
            >>> with program.lr_schedule_guard():
            >>>     lr = lr * decay
        """
4227 4228

        tmp_role = self._current_role
4229
        tmp_var = self.__op_role_var
4230

4231 4232
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.LRSched
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        if is_with_opt:
            self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
4235
        # TODO(typhoonzero): how to set target learning rate var
4236
        self.__op_role_var = []
4237 4238 4239 4240 4241
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
4242

4243
    def __str__(self):
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        """
        Get the protobuf debug string of this Program.

        Returns:
            (str): The protobuf debug string.

        Raises:
            ValueError: If any of required fields is not set.
        """
4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291
        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)
4292
            program_str += '\n'
4293
        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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4304

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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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4311 4312 4313
        Examples:
            .. code-block:: python

4314 4315 4316 4317
                import paddle
                import paddle.static as static

                paddle.enable_static()
4318

4319 4320 4321
                prog = static.default_main_program()
                x = static.data(name="X", shape=[2,3], dtype="float32")
                pred = static.nn.fc(x, size=3)
4322
                prog_string = prog.to_string(throw_on_error=True, with_details=False)
4323
                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))
4325
                print("program string with detail: {}".format(prog_string_with_details))
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        """
4327 4328 4329 4330 4331 4332 4333 4334 4335
        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()
4342 4343
            proto = framework_pb2.ProgramDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
4346

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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.
        """
4355 4356
        return self.desc

X
version  
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4357 4358 4359
    def _version(self):
        return self.desc._version()

4360
    def clone(self, for_test=False):
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4361
        """
4362 4363 4364 4365
        .. note:::
            1. :code:`Program.clone()` method DOES NOT clone :ref:`api_paddle_io_DataLoader` . 
            2. Recommend you to use :code:`clone` before using :code:`Opimizer.minimize` . 
            3. This API has no effect in Dygraph Mode.
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4366

4367
        Create a new Program with forward content of original one when ``for_test=True``.
4368
        Create a new Program as same as the original one when ``for_test=False``.
4369

4370
        Some operators, e.g., :ref:`api_paddle_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`.
4374

4375 4376
        * 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.
4377 4378
          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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4379
          recommend you to use :code:`clone` before using :code:`Opimizer.minimize`.
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4380

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4381
        For Example:
4382
          ::
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4383

4384 4385 4386 4387 4388 4389
            import paddle
            import paddle.static as static

            paddle.enable_static()

            img = static.data(name='image', shape=[None, 784])
4390
            pred = static.nn.fc(x=img, size=10, actvation='relu')
4391
            loss = paddle.mean(pred)
4392
            # Here we use clone before Momentum
4393 4394
            test_program = static.default_main_program().clone(for_test=True)
            optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
4395
            optimizer.minimize(loss)
4396

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4397
        Args:
4398

4399 4400
            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` .
4401

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4402
        Returns:
4403
            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``
4404

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4405 4406 4407

        Examples:

4408 4409 4410 4411 4412 4413 4414
            .. note::
                The Program's order maybe different after :code:`clone` and
                this will not affect your training or testing progress. In the following
                example we give you an simple method :code:`print_prog(program)` to
                print Program Descs inorder to make sure you have same print result
                after :code:`clone`:

4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430
            .. code-block:: python

                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))


4431
            1. To clone a test program, the sample code is:
4432 4433 4434
                .. code-block:: python

                    import six
4435 4436 4437 4438 4439 4440
                    import paddle
                    import paddle.static as static
                    import paddle.utils as utils
                    import paddle.nn.functional as F

                    paddle.enable_static()
4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452

                    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))

4453 4454
                    train_program = static.Program()
                    startup_program = static.Program()
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4455 4456 4457

                    # startup_program is used to do some parameter init work,
                    # and main program is used to hold the network
4458 4459 4460
                    with static.program_guard(train_program, startup_program):
                        with utils.unique_name.guard():
                            img = static.data(name='image', shape=[None, 784])
4461
                            hidden = static.nn.fc(x=img, size=200, activation='relu')
4462 4463
                            hidden = F.dropout(hidden, p=0.5)
                            loss = F.cross_entropy(
4464
                                input=static.nn.fc(x=hidden, size=10, activation='softmax'),
4465 4466
                                label=static.data(name='label', shape=[1], dtype='int64'))
                            avg_loss = paddle.mean(loss)
4467
                            test_program = train_program.clone(for_test=True)
4468
                    print_prog(test_program)
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4469 4470 4471 4472 4473 4474 4475 4476 4477

                    # 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.

4478 4479 4480
                    with static.program_guard(train_program, startup_program):
                        with utils.unique_name.guard():
                            sgd = paddle.optimizer.SGD(learning_rate=1e-3)
4481 4482 4483
                            sgd.minimize(avg_loss)


4484
            2. The clone method can be avoid if you create program for training and program for testing individually.
4485 4486 4487
                .. code-block:: python

                    import six
4488 4489 4490 4491 4492 4493
                    import paddle
                    import paddle.static as static
                    import paddle.utils as utils
                    import paddle.nn.functional as F

                    paddle.enable_static()
4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 4504

                    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))
4505

4506
                    def network():
4507
                        img = static.data(name='image', shape=[None, 784])
4508
                        hidden = static.nn.fc(x=img, size=200, activation='relu')
4509 4510
                        hidden = F.dropout(hidden, p=0.5)
                        loss = F.cross_entropy(
4511
                            input=static.nn.fc(x=hidden, size=10, activation='softmax'),
4512 4513
                            label=static.data(name='label', shape=[1], dtype='int64'))
                        avg_loss = paddle.mean(loss)
4514 4515
                        return avg_loss

4516 4517 4518 4519 4520
                    train_program_2 = static.Program()
                    startup_program_2 = static.Program()
                    test_program_2 = static.Program()
                    with static.program_guard(train_program_2, startup_program_2):
                        with utils.unique_name.guard():
4521
                            avg_loss = network()
4522
                            sgd = paddle.optimizer.SGD(learning_rate=1e-3)
4523
                            sgd.minimize(avg_loss)
4524
                    # the test startup program is not used.
4525 4526
                    with static.program_guard(test_program_2, startup_program_2):
                        with utils.unique_name.guard():
4527 4528
                            avg_loss = network()
                    print_prog(test_program_2)
4529

4530
            The two code snippets above will generate and print same programs.
4531
        """
4532 4533 4534 4535 4536

        #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()

4537
        pruned_origin_block_id_map = None
4538
        if for_test:
4539 4540 4541 4542 4543 4544 4545 4546 4547
            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)
4548
        else:
4549
            p = Program()
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4550 4551
            p.current_block_idx = self.current_block_idx
            p._seed = self._seed
4552
            p.desc = core.ProgramDesc(self.desc)
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4553 4554 4555
            p.blocks = [
                Block(p, i) for i in six.moves.range(self.desc.num_blocks())
            ]
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4556 4557

            p._current_role = self._current_role
4558
            p.__op_role_var = self.__op_role_var
4559
            p._appending_grad_times = self._appending_grad_times
4560 4561
            if hasattr(self, 'lr_sheduler'):
                p.lr_sheduler = self.lr_sheduler
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4562

4563 4564
            #NOTE(zhiqiu): we sync the cloned program, to update its program by
            # its desc.
W
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4565
            p._sync_with_cpp()
4566

W
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4567
        p._copy_param_info_from(self)
4568
        p._copy_data_info_from(self, pruned_origin_block_id_map)
4569
        p._copy_dist_param_info_from(self)
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4570
        return p
4571

4572
    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:
4581
            targets(list|Variable|Operator): A list of variables, operators, or variable names
Y
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4582 4583 4584 4585
                need to be pruned

        Returns:
            Program:  A new, pruned program.
4586
        """
4587
        return self._prune_with_input([], targets)
4588 4589

    def _prune_with_input(self, feeded_var_names, targets):
Y
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4590
        """
4591 4592 4593 4594 4595 4596 4597 4598 4599 4600
        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()
4601
            targets(list|Variable|Operator): A list of variables, operators, or variable names
4602 4603 4604 4605 4606 4607
                need to be pruned

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

4608 4609 4610 4611
        #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()

4612 4613
        if not isinstance(feeded_var_names, list):
            feeded_var_names = [feeded_var_names]
4614 4615
        if not isinstance(targets, list):
            targets = [targets]
4616 4617 4618

        for var in feeded_var_names:
            if not isinstance(var, six.string_types):
4619 4620 4621
                raise ValueError(
                    "All feeded_var_names of Program._prune_with_input() can only be "
                    "str, but received %s." % type(var))
4622

4623 4624 4625 4626
        targets_idx = []
        for t in targets:
            if not isinstance(t, Operator):
                if isinstance(t, Variable):
4627 4628 4629
                    name = t.name
                elif isinstance(t, six.string_types):
                    name = str(t)
4630
                else:
4631 4632 4633
                    raise ValueError(
                        "All targets of Program._prune_with_input() can only be "
                        "Variable or Operator, but received %s." % type(t))
4634 4635 4636 4637 4638 4639 4640 4641

                # 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

4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657
                # 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
4658 4659 4660 4661 4662 4663 4664 4665
                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])
4666

4667
        res = Program()
4668 4669 4670
        res.desc, pruned_origin_block_id_map = core.prune(self.desc,
                                                          set(feeded_var_names),
                                                          targets_idx)
M
minqiyang 已提交
4671 4672 4673
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
Wu Yi 已提交
4674
        res._sync_with_cpp()
4675 4676 4677 4678 4679

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

4680 4681
        return res

X
Xin Pan 已提交
4682
    def _inference_optimize(self, prune_read_op=True):
Y
yuyang18 已提交
4683
        """
F
fengjiayi 已提交
4684 4685 4686 4687 4688
        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.

4689
        3. change the :code:`is_test`
Y
yuyang18 已提交
4690 4691 4692
        attribute of operators to :code:`True`. All the :code:`Parameter`
        information will be lost.

4693
        Args:
X
Xin Pan 已提交
4694 4695
            prune_read_op(bool): remove the read ops that are added by py_reader
                                 for cpp inference library
4696

Y
yuyang18 已提交
4697 4698 4699 4700 4701 4702
        Notes: This API is a very low level API. Use
        :code:`Program.clone(for_test=True)` instead.

        Returns:
            Program: The new program.
        """
4703
        res = Program()
4704
        res.desc = core.ProgramDesc(self.desc)
F
fengjiayi 已提交
4705 4706 4707 4708

        # remove all readers and the read_op if exist
        read_op_idx = 0
        root_block = res.desc.block(0)
X
Xin Pan 已提交
4709
        if prune_read_op:
4710 4711 4712 4713 4714 4715 4716 4717 4718
            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:
M
minqiyang 已提交
4719
                    root_block._remove_var(cpt.to_bytes(var.name()))
F
fengjiayi 已提交
4720 4721

        # change all `is_test` attributes to True
M
minqiyang 已提交
4722
        for i in six.moves.range(res.desc.num_blocks()):
4723
            block = res.desc.block(i)
M
minqiyang 已提交
4724
            for j in six.moves.range(block.op_size()):
4725 4726
                op = block.op(j)
                if op.has_attr('is_test'):
W
Wu Yi 已提交
4727
                    op._set_attr('is_test', True)
M
minqiyang 已提交
4728 4729 4730
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
Wu Yi 已提交
4731
        res._sync_with_cpp()
4732 4733
        return res

4734 4735
    @staticmethod
    def parse_from_string(binary_str):
Y
yuyang18 已提交
4736
        """
4737 4738 4739
        .. note::
            1. All information about parameters will be lost after serialization; 
            2. This API has no effect in Dygraph mode.
Y
yuyang18 已提交
4740

4741 4742
        Deserialize a Program from  `protobuf <https://en.wikipedia.org/wiki/Protocol_Buffers>`_  binary string.
        This method always use to save and load model
Y
yuyang18 已提交
4743

J
Jiabin Yang 已提交
4744
        Args:
Y
yuyang18 已提交
4745

J
Jiabin Yang 已提交
4746
            binary_str_type (str): the binary prootbuf string.
4747

J
Jiabin Yang 已提交
4748 4749
        Returns:
            Program: A deserialized Program.
4750 4751 4752 4753

        Examples:
            .. code-block:: python

4754 4755 4756 4757
                import paddle
                import paddle.static as static

                paddle.enable_static()
4758

4759 4760 4761 4762
                startup_prog = static.Program()
                main_prog = static.Program()
                with static.program_guard(startup_prog, main_prog):
                    x = static.data(name='X', shape=[1000, 784], dtype='float32')
4763

4764
                    y = static.data(name='Y', shape=[784, 100], dtype='float32')
4765

4766
                    z = paddle.matmul(x=x, y=y)
4767

4768 4769
                    binary_str = static.default_main_program().desc.serialize_to_string()
                    prog_restored = static.default_main_program().parse_from_string(binary_str)
4770

4771
                    print(static.default_main_program())
4772
                    print(prog_restored)
Y
yuyang18 已提交
4773
        """
4774 4775
        p = Program()
        p.desc = core.ProgramDesc(binary_str)
M
minqiyang 已提交
4776
        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
W
Wu Yi 已提交
4777
        p._sync_with_cpp()
4778
        return p
Y
Yu Yang 已提交
4779

4780
    @staticmethod
4781
    def _construct_from_desc(desc):
4782 4783 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796
        """
        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

D
dzhwinter 已提交
4797 4798
    @property
    def random_seed(self):
Y
yuyang18 已提交
4799
        """
J
Jiabin Yang 已提交
4800
        The default random seed for random operators in Program. ``0`` means get
Y
yuyang18 已提交
4801 4802
        the random seed from random device.

4803 4804
        .. note:: 
            It must be set before the operators have been added.
J
Jiabin Yang 已提交
4805 4806 4807

        Returns:
            int64: Random seed in current Program
4808

4809 4810 4811 4812

        Examples:
            .. code-block:: python

4813 4814 4815
                import paddle
                import paddle.static as static
                import paddle.nn.functional as F
4816

4817 4818 4819
                paddle.enable_static()

                prog = static.default_main_program()
4820
                random_seed = prog.random_seed
4821
                x_var = static.data(name="X", shape=[3,3], dtype="float32")
4822 4823 4824
                print(random_seed)
                ## 0
                ## the default random seed is 0
4825 4826

                # Here we need to set random seed before we use fluid.layers.dropout
4827
                prog.random_seed = 1
4828
                z_var = F.dropout(x_var, 0.7)
4829

4830
                print(prog.random_seed)
4831 4832
                ## 1
                ## the random seed is change to 1
Y
yuyang18 已提交
4833
        """
D
dzhwinter 已提交
4834 4835
        return self._seed

Q
qiaolongfei 已提交
4836 4837
    @property
    def num_blocks(self):
Y
yuyang18 已提交
4838
        """
4839 4840
        The number of :ref:`api_guide_Block_en`  in this Program.

4841 4842
        .. note:: 
            This API has no effect in Dygraph mode.
J
Jiabin Yang 已提交
4843 4844 4845

        Returns:
            int(Platform-dependent size): num of :ref:`api_guide_Block_en`  in current Program
4846

4847 4848 4849 4850

        Examples:
            .. code-block:: python

4851 4852 4853 4854
                import paddle
                import paddle.static as static

                paddle.enable_static()
4855

4856
                prog = static.default_main_program()
4857 4858
                num_blocks = prog.num_blocks
                print(num_blocks)
4859

4860 4861
                # print result:
                # 1
Y
yuyang18 已提交
4862
        """
Q
qiaolongfei 已提交
4863 4864
        return self.desc.num_blocks()

D
dzhwinter 已提交
4865 4866 4867
    @random_seed.setter
    def random_seed(self, seed):
        if not isinstance(seed, int):
4868 4869 4870
            raise ValueError(
                "Program.random_seed's input seed must be an integer, but received %s."
                % type(seed))
D
dzhwinter 已提交
4871 4872
        self._seed = seed

Y
Yu Yang 已提交
4873
    def __repr__(self):
4874
        return self.__str__()
4875

Y
Yu Yang 已提交
4876
    def global_block(self):
Y
yuyang18 已提交
4877
        """
4878 4879
        .. note::
            This API has no effect in Dygraph mode.
4880 4881 4882

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

J
Jiabin Yang 已提交
4883 4884
        Returns:
            :ref:`api_guide_Block_en`: The first :ref:`api_guide_Block_en`  of this Program.
4885

4886 4887 4888 4889

        Examples:
            .. code-block:: python

4890 4891 4892 4893
                import paddle
                import paddle.static as static

                paddle.enable_static()
4894

4895
                prog = static.default_main_program()
4896 4897
                gb_block = prog.global_block()
                print(gb_block)
4898

Y
yuyang18 已提交
4899
        """
Y
Yu Yang 已提交
4900 4901
        return self.blocks[0]

Q
Qiao Longfei 已提交
4902
    def block(self, index):
Y
yuyang18 已提交
4903
        """
4904 4905
        .. note::
            This API has no effect in Dygraph mode.
Y
yuyang18 已提交
4906

4907 4908
        Get the :code:`index`  :ref:`api_guide_Block_en`  of this Program

J
Jiabin Yang 已提交
4909 4910
        Args:
            index (int) - The index of  :ref:`api_guide_Block_en`  to get
4911

J
Jiabin Yang 已提交
4912 4913
        Returns:
            :ref:`api_guide_Block_en`: The :code:`index` block
4914 4915 4916 4917

        Examples:
            .. code-block:: python

4918 4919 4920 4921
                import paddle
                import paddle.static as static

                paddle.enable_static()
4922

4923
                prog = static.default_main_program()
4924 4925
                block_0 = prog.block(0)
                print(block_0)
Y
yuyang18 已提交
4926
        """
Q
Qiao Longfei 已提交
4927 4928
        return self.blocks[index]

Y
Yu Yang 已提交
4929
    def current_block(self):
Y
yuyang18 已提交
4930
        """
4931 4932
        .. note::
            This API has no effect in Dygraph mode.
4933

J
Jiabin Yang 已提交
4934 4935
        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.
4936

J
Jiabin Yang 已提交
4937 4938
        Returns:
             :ref:`api_guide_Block_en`: The :code:`index`  :ref:`api_guide_Block_en`
4939

4940 4941 4942
        Examples:
            .. code-block:: python

4943 4944 4945 4946
                import paddle
                import paddle.static as static

                paddle.enable_static()
4947

4948
                prog = static.default_main_program()
4949 4950
                current_blk = prog.current_block()
                print(current_blk)
Y
yuyang18 已提交
4951
        """
Y
Yu Yang 已提交
4952 4953
        return self.blocks[self.current_block_idx]

W
Wu Yi 已提交
4954
    def _create_block(self, parent_idx=None):
Y
yuyang18 已提交
4955 4956 4957 4958 4959
        """
        Create a new block with the :code:`parent_idx` and change the current block
        to new block.

        Args:
J
Jiabin Yang 已提交
4960

Y
yuyang18 已提交
4961 4962 4963 4964 4965
            parent_idx(int): The parent block index.

        Returns:
            Block: The new block.
        """
Y
Yu Yang 已提交
4966
        new_block_idx = len(self.blocks)
F
update  
fengjiayi 已提交
4967 4968 4969
        parent = self.current_block() if parent_idx is None else self.block(
            parent_idx)
        self.desc.append_block(parent.desc)
Y
Yu Yang 已提交
4970 4971 4972 4973
        self.current_block_idx = new_block_idx
        self.blocks.append(Block(self, self.current_block_idx))
        return self.current_block()

W
Wu Yi 已提交
4974
    def _rollback(self):
Y
yuyang18 已提交
4975 4976 4977 4978 4979
        """
        Exit a code block, i.e., roll back to the parent block.
        Returns:
            None
        """
Y
Yu Yang 已提交
4980 4981
        self.current_block_idx = self.current_block().parent_idx

W
Wu Yi 已提交
4982
    def _sync_with_cpp(self):
Y
yuyang18 已提交
4983 4984 4985 4986 4987 4988 4989 4990 4991 4992
        """
        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
        """
Q
Qiao Longfei 已提交
4993 4994 4995
        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
W
Wu Yi 已提交
4996
            block._sync_with_cpp()
Q
Qiao Longfei 已提交
4997

W
Wu Yi 已提交
4998
    def _copy_param_info_from(self, other):
4999
        """
5000
        Copy the information of parameters from other program.
D
dzhwinter 已提交
5001

Y
yuyang18 已提交
5002 5003 5004
        Notes: This is a very low level API. Users should not invoke it
        directly.

5005 5006 5007 5008 5009 5010 5011
        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
5012 5013 5014
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
5015

W
Wu Yi 已提交
5016
        self.global_block()._copy_param_info_from(other.global_block())
5017

5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028
    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):
5029 5030 5031
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
5032 5033
        self._is_distributed = other._is_distributed
        self._is_chief = other._is_chief
5034
        self._parameters_on_pservers = other._parameters_on_pservers
5035
        self._endpoints = other._endpoints
5036
        self._ps_endpoint = other._ps_endpoint
5037 5038
        self._distributed_lookup_table = other._distributed_lookup_table

5039
    def _copy_data_info_from(self, other, pruned_origin_block_id_map=None):
F
fengjiayi 已提交
5040 5041
        """
        Copy the information of data variables from other program.
D
dzhwinter 已提交
5042

Y
yuyang18 已提交
5043 5044 5045
        Notes: This is a very low level API. Users should not invoke it
        directly.

F
fengjiayi 已提交
5046 5047
        Args:
            other(Program): Other program
5048 5049 5050 5051
            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
fengjiayi 已提交
5052 5053 5054 5055 5056

        Returns:
            None
        """
        if not isinstance(other, Program):
5057 5058 5059
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
F
fengjiayi 已提交
5060

5061 5062 5063 5064 5065
        if not pruned_origin_block_id_map:
            pruned_origin_block_id_map = {
                i: i
                for i in six.moves.range(self.desc.num_blocks())
            }
5066 5067 5068

        # NOTE(zhiqiu): All vars in cloned program exist in original program.
        # The reverse is not true, due to backward pruning.
5069 5070
        for i, block in enumerate(self.blocks):
            other_block = other.blocks[pruned_origin_block_id_map[i]]
5071
            for var in list(block.vars.values()):
5072 5073 5074 5075 5076 5077 5078
                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
F
fengjiayi 已提交
5079

5080
    def list_vars(self):
Y
yuyang18 已提交
5081
        """
5082
        Get all Tensors from this Program. A iterable object is returned.
Y
yuyang18 已提交
5083

J
Jiabin Yang 已提交
5084
        Returns:
5085
            iterable Tensors: The Generator will yield every Tensor in this program.
5086 5087 5088 5089

        Examples:
            .. code-block:: python

5090 5091
                import paddle
                import paddle.static as static
5092

5093 5094 5095 5096 5097
                paddle.enable_static()

                prog = static.default_main_program()
                img = static.data(name='img', shape=[None, 1,28,28], dtype='float32')
                label = static.data(name='label', shape=[None,1], dtype='int64')
5098 5099
                for var in prog.list_vars():
                    print(var)
5100 5101 5102
                
                # var img : fluid.VarType.LOD_TENSOR.shape(-1, 1, 28, 28).astype(VarType.FP32)
                # var label : fluid.VarType.LOD_TENSOR.shape(-1, 1).astype(VarType.INT64)
Y
yuyang18 已提交
5103
        """
5104
        for each_block in self.blocks:
5105
            for each_var in list(each_block.vars.values()):
5106 5107
                yield each_var

5108 5109 5110 5111 5112 5113 5114 5115 5116 5117
    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

5118 5119 5120 5121
                import paddle
                import paddle.static as static

                paddle.enable_static()
5122

5123 5124
                program = static.default_main_program()
                data = static.data(name='x', shape=[None, 13], dtype='float32')
5125
                hidden = static.nn.fc(x=data, size=10)
5126 5127
                loss = paddle.mean(hidden)
                paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
5128 5129 5130 5131 5132 5133 5134

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

                # Here will print all parameters in current program, in this example,
                # the result is like:
                #
5135 5136
                # persist trainable param fc_0.w_0 : fluid.VarType.LOD_TENSOR.shape(13, 10).astype(VarType.FP32)
                # persist trainable param fc_0.b_0 : fluid.VarType.LOD_TENSOR.shape(10,).astype(VarType.FP32)
5137 5138 5139 5140 5141 5142 5143 5144 5145 5146
                #
                # 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

Y
Yu Yang 已提交
5147

5148
@six.add_metaclass(ParameterMetaClass)
Y
Yu Yang 已提交
5149
class Parameter(Variable):
5150
    """
5151
    Parameter is derived from Variable. A parameter is a persistable
5152
    Variable, and will be updated by optimizers after each iteration.
5153
    The training of a neural network is essentially the updating of
5154 5155
    its parameters.

5156
    Relative to a general Variable, a Parameter has several its own
5157 5158
    member variables:

5159 5160 5161 5162 5163 5164 5165 5166 5167 5168
    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.
5169 5170
        need_clip (bool): Whether the parameter gradient need to be cliped 
            in optimizer. Default is True.
5171 5172
    """

5173 5174 5175 5176 5177 5178
    def __init__(self,
                 block,
                 shape,
                 dtype,
                 type=core.VarDesc.VarType.LOD_TENSOR,
                 **kwargs):
5179 5180 5181 5182 5183
        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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        if len(shape) == 0:
5185 5186
            raise ValueError(
                "The dimensions of shape for Parameter must be greater than 0")
Y
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5187 5188 5189

        for each in shape:
            if each < 0:
5190 5191 5192
                raise ValueError(
                    "Each dimension of shape for Parameter must be greater than 0, but received %s"
                    % list(shape))
5193 5194

        Variable.__init__(
5195 5196 5197 5198 5199 5200 5201
            self,
            block,
            persistable=True,
            shape=shape,
            dtype=dtype,
            type=type,
            **kwargs)
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        self.trainable = kwargs.get('trainable', True)

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

5206 5207
        self.regularizer = kwargs.get('regularizer', None)

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        self.do_model_average = kwargs.get('do_model_average', None)
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5210 5211
        self.need_clip = kwargs.get('need_clip', True)

5212 5213
        self.is_distributed = False

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    def __str__(self):
5215
        return self._to_readable_code()
F
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5216

F
update  
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5217 5218 5219
    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
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F
update  
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5221 5222 5223 5224 5225 5226 5227 5228
        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.

5229 5230 5231 5232 5233 5234 5235 5236 5237
        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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5238 5239 5240 5241 5242 5243
        """
        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",
5244
                               "do_model_average", "need_clip")
F
update  
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5245
            for attr_name in additional_attr:
5246 5247
                res_str += "%s: %s\n" % (attr_name,
                                         cpt.to_text(getattr(self, attr_name)))
F
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5248 5249
        else:
            res_str = Variable.to_string(self, throw_on_error, False)
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        return res_str

    __repr__ = __str__

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5255 5256
class ParamBase(core.VarBase):
    """
5257 5258 5259
    ParamBase is derived from Tensor( Which is the concept in Dygraph Mode). 
    A ParamBase is a persistable Tensor, and will be updated by optimizers 
    after each iteration.
5260 5261 5262
    The training of a neural network is essentially the updating of
    its ParamBase.

5263
    Relative to a general Tensor, a ParamBase has several its own
5264 5265 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275
    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.
5276 5277
        need_clip (bool): Whether the parameter gradient need to be cliped 
            in optimizer. Default is True.
5278 5279 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291 5292 5293 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304 5305 5306 5307
    """

    @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)

5308 5309
        trainable = kwargs.get('trainable', True)
        self.stop_gradient = not trainable
5310 5311 5312 5313 5314 5315 5316

        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)

5317 5318
        self.need_clip = kwargs.get('need_clip', True)

5319
        self.is_distributed = False
5320
        # self.block = default_main_program().global_block()
5321

5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 5332 5333 5334
    @property
    def trainable(self):
        return not self.stop_gradient

    @trainable.setter
    def trainable(self, trainable):
        if isinstance(trainable, bool):
            self.stop_gradient = not trainable
        else:
            raise ValueError(
                "The type of trainable MUST be bool, but the type is ",
                type(trainable))

5335
    def __str__(self):
5336
        """
5337
        Convert a ParamBase object to a readable string.
5338

5339
        Returns(str): A readable string.
5340 5341 5342 5343

        Examples:
            .. code-block:: python

5344
                import paddle
5345 5346 5347 5348 5349 5350 5351
                linear = paddle.nn.Linear(3, 3)
                print(linear.weight)
                # Parameter containing:
                # Tensor(shape=[3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
                #        [[ 0.48948765,  0.05829060, -0.25524026],
                #         [-0.70368278,  0.52986908, -0.68742192],
                #         [-0.54217887,  0.48439729,  0.34082305]])
5352
        """
5353 5354
        return "Parameter containing:\n{tensor}".format(
            tensor=super(ParamBase, self).__str__())
5355 5356 5357 5358

    __repr__ = __str__


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# program is a global instance.
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_main_program_ = Program()
_startup_program_ = Program()
5362

5363

5364
def default_startup_program():
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5365
    """
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    Get default/global startup program.

5368 5369 5370 5371 5372
    The :code:`paddle.nn` function will append the initialization operators into startup program.
    The :code:`startup_program` will initialize the parameters by the OPs. 
  
    This method will return the default or the current startup program. Users can use
    :ref:`api_paddle_fluid_framework_program_guard`  to switch :ref:`api_paddle_fluid_framework_Program` .
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5374 5375
    Returns:
        Program: current default startup program.
5376

5377
    Returns type: 
5378 5379 5380 5381

    Examples:
        .. code-block:: python

5382
            import paddle
5383

5384 5385 5386 5387
            paddle.enable_static()
            main_program = paddle.static.Program()
            startup_program = paddle.static.Program()
            with paddle.static.program_guard(main_program=main_program, startup_program=startup_program):
5388 5389
                x = paddle.static.data(name="x", shape=[-1, 784], dtype='float32')
                y = paddle.static.data(name="y", shape=[-1, 1], dtype='int32')
5390
                z = paddle.static.nn.fc(name="fc", x=x, size=10, activation="relu")
5391

5392 5393
                print("main program is: {}".format(paddle.static.default_main_program()))
                print("start up program is: {}".format(paddle.static.default_startup_program()))
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    """
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5395
    return _startup_program_
5396

5397

5398
def default_main_program():
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5399
    """
5400
    This API can be used to get ``default main program`` which store the 
5401
    descriptions of Ops and tensors.
5402
    
5403
    For example ``z = paddle.fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add`` 
5404
    Op and a new ``z`` tensor, and they will be recorded in ``default main program`` . 
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5405

5406 5407
    The ``default main program`` is the default value for ``Program`` parameter in 
    a lot of APIs. For example, the :code:`Executor.run()` will execute the
Y
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5408
    :code:`default_main_program` when the program is not specified.
5409

5410
    If you want to switch the ``default main program``, you can use :ref:`api_paddle_fluid_framework_program_guard` .
5411
    
Y
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    Returns:
5413
        Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
5414 5415 5416 5417

    Examples:
        ..  code-block:: python

5418 5419 5420
            import paddle
            
            paddle.enable_static()
5421
            # Sample Network:
5422 5423
            data = paddle.static.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
            label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
5424
            
5425 5426
            conv1 = paddle.static.nn.conv2d(data, 4, 5, 1, act=None)
            bn1 = paddle.static.nn.batch_norm(conv1, act='relu')
5427
            pool1 = paddle.fluid.layers.pool2d(bn1, 2, 'max', 2)
5428 5429
            conv2 = paddle.static.nn.conv2d(pool1, 16, 5, 1, act=None)
            bn2 = paddle.static.nn.batch_norm(conv2, act='relu')
5430
            pool2 = paddle.fluid.layers.pool2d(bn2, 2, 'max', 2)
5431
            
5432 5433
            fc1 = paddle.static.nn.fc(x=pool2, size=50, activation='relu')
            fc2 = paddle.static.nn.fc(x=fc1, size=102, activation='softmax')
5434
            
5435 5436 5437
            loss = paddle.nn.functional.loss.cross_entropy(input=fc2, label=label)
            loss = paddle.mean(loss)
            opt = paddle.optimizer.Momentum(
5438 5439
                learning_rate=0.1,
                momentum=0.9,
5440
                weight_decay=paddle.regularizer.L2Decay(1e-4))
5441 5442
            opt.minimize(loss)
            
5443
            #print the number of blocks in the program, 1 in this case
5444
            print(paddle.static.default_main_program().num_blocks) #[1]
5445 5446

            #print the description of variable 'image'
5447
            print(paddle.static.default_main_program())
5448

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5449
    """
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5450
    return _main_program_
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def switch_main_program(program):
    """
    Switch the main program to a new program.
5456

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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):
    """
5471
    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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5484
@signature_safe_contextmanager
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5485 5486
def program_guard(main_program, startup_program=None):
    """
5487 5488
    :api_attr: Static Graph

5489 5490 5491
    Change the global main program and startup program with ``with`` statement.
    Layer functions in the Python ``with`` block will append operators and
    Tensors to the new main programs.
5492

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    Args:
5494 5495
        main_program(Program): New main program inside ``with`` statement.
        startup_program(Program, optional): New startup program inside ``with`` 
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            statement. :code:`None` means not changing startup program, 
            default_startup_program is still used.
            Default: None.

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5500
    Examples:
5501 5502
       .. code-block:: python
       
5503
          import paddle
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5505 5506 5507 5508 5509
          paddle.enable_static()
          main_program = paddle.static.Program()
          startup_program = paddle.static.Program()
          with paddle.static.program_guard(main_program, startup_program):
              data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
5510
              hidden = paddle.static.nn.fc(x=data, size=10, activation='relu')
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5511 5512 5513

    Notes: The temporary :code:`Program` can be used if the user does not need
    to construct either of startup program or main program.
5514

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5515
    Examples:
5516
       .. code-block:: python
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5517

5518
          import paddle
5519

5520 5521 5522 5523 5524
          paddle.enable_static()
          main_program = paddle.static.Program()
          # does not care about startup program. Just pass a temporary value.
          with paddle.static.program_guard(main_program, paddle.static.Program()):
              data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
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5526
    """
5527
    from .data_feeder import check_type
5528 5529
    check_type(main_program, 'main_program', Program,
               'paddle.static.program_guard')
Y
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5530 5531
    main_program = switch_main_program(main_program)
    if startup_program is not None:
5532
        check_type(startup_program, 'startup_program', Program,
5533
                   'paddle.static.program_guard')
Y
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5534
        startup_program = switch_startup_program(startup_program)
5535 5536 5537 5538 5539 5540
    try:
        yield
    finally:
        switch_main_program(main_program)
        if startup_program is not None:
            switch_startup_program(startup_program)
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5541 5542


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5543
def _get_var(name, program=None):
X
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5544
    """
Y
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5545
    Get a variable by name from the global block of a program.
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5546

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5547 5548 5549
    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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5551 5552 5553 5554 5555 5556 5557

    Returns:
        Variable
    """
    if program is None:
        program = default_main_program()
    assert isinstance(name, str)
5558
    assert isinstance(program, Program)
X
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5559 5560

    return program.global_block().var(name)
5561 5562


S
rename  
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5563
@signature_safe_contextmanager
L
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5564 5565 5566 5567
def _dygraph_guard(tracer):
    global _dygraph_tracer_
    tmp_trace = _dygraph_tracer_
    _dygraph_tracer_ = tracer
5568
    core._switch_tracer(tracer)
M
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5569

5570 5571 5572 5573 5574
    try:
        yield
    finally:
        core._switch_tracer(tmp_trace)
        _dygraph_tracer_ = tmp_trace
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5575 5576


S
rename  
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5577
@signature_safe_contextmanager
L
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5578
def _dygraph_place_guard(place):
5579 5580 5581
    global _global_expected_place_
    tmp_place = _global_expected_place_
    _global_expected_place_ = place
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5582

5583 5584 5585
    try:
        yield
    finally:
5586
        _global_expected_place_ = tmp_place
5587 5588 5589 5590


def load_op_library(lib_filename):
    """
5591 5592
    :api_attr: Static Graph
    
5593 5594 5595
    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.
T
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5596
    Please note, the type of custom operators can't have the same type
5597 5598 5599 5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610
    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()
5611 5612 5613 5614 5615 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 5628 5629 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641 5642 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655 5656 5657 5658 5659 5660 5661 5662


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])
    """

5663 5664 5665 5666 5667
    index = None
    if device and ':' in device:
        device, index = device.split(':')
        if device == 'cpu':
            raise ValueError("Should not set device id for cpu.")
5668 5669 5670 5671
    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)
5672 5673
    if index:
        device = ":".join([device, index])
5674
    pre_device = switch_device(device)
5675 5676 5677 5678
    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