framework.py 127.7 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
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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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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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__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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]
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EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
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CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()

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_dygraph_tracer_ = None
_dygraph_current_expected_place_ = None
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def in_dygraph_mode():
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    """
    Check program status(tracer), Whether it runs in dygraph mode or not

    Returns:
        out (boolean): True if the program is running in dynamic graph mode

    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            if fluid.in_dygraph_mode():
                pass

    """
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    return _dygraph_tracer_ is not None
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def _dygraph_tracer():
    return _dygraph_tracer_
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def _current_expected_place():
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    return _dygraph_current_expected_place_
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def _cpu_num():
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    if "CPU_NUM" not in os.environ.keys():
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        sys.stderr.write(
            'The CPU_NUM is not specified, you should set CPU_NUM in '
            'the environment variable list, i.e export CPU_NUM=1. CPU_NUM '
            'indicates that how many CPUPlace are used in the current task.\n'
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            '!!! The default number of CPUPlaces is 1.\n\n')
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        os.environ['CPU_NUM'] = str(1)
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    cpu_num = os.environ.get('CPU_NUM')
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    return int(cpu_num)


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

    Returns (bool): support gpu or not.

    Examples:
        .. code-block:: python

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


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def cuda_places(device_ids=None):
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    """
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    Create a list of :code:`fluid.CUDAPlace` objects.

    If :code:`device_ids` is None, environment variable of
    :code:`FLAGS_selected_gpus` would be checked first. If
    :code:`FLAGS_selected_gpus=0,1,2`, the returned list would
    be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    If :code:`FLAGS_selected_gpus` is not set, all visible
    gpu places would be returned.  

    If :code:`device_ids` is not None, it should be the device
    ids of gpus. For example, if :code:`device_ids=[0,1,2]`, 
    the returned list would be 
    [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
    
    Args: 
        device_ids (None|list(int)|tuple(int)): gpu device id list.

    Returns:
        out (list(fluid.CUDAPlace)): gpu place list.
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    Examples:
        .. code-block:: python

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

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


def cpu_places(device_count=None):
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    """
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    Create a list of :code:`fluid.CPUPlace` objects.
    
    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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    Args:
        device_count (None|int): device number.

    Returns:
        out (list(fluid.CPUPlace)): cpu place list.
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    Examples:
        .. code-block:: python

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

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


def cuda_pinned_places(device_count=None):
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    """
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    Create a list of :code:`fluid.CUDAPinnedPlace` objects.

    If :code:`device_count` is None, the device count would
    be determined by environment variable :code:`CPU_NUM`. 
    If :code:`CPU_NUM` is not set, the device count would
    be determined by :code:`multiprocessing.cpu_count()`. 

    Args:
        device_count (None|int): device number.

    Returns:
        out (list(fluid.CUDAPinnedPlace)): cuda pinned place list.
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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:
        device_count = _cpu_num()
    return [core.cuda_pinned_places()] * device_count


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class NameScope(object):
    def __init__(self, name="", parent=None):
        self._children = dict()
        self._name = name
        self._parent = parent

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

    def parent(self):
        return self._parent

    def name(self):
        return self._name


_name_scope = NameScope()


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

    Note: This should only used for debugging and visualization purpose.
    Don't use it for serious analysis such as graph/program transformations.

    Args:
        prefix(str): prefix.

    Examples:
        .. code-block:: python
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          import paddle.fluid as fluid
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          with fluid.name_scope("s1"):
              a = fluid.layers.data(name='data', shape=[1], dtype='int32')
              b = a + 1
              with fluid.name_scope("s2"):
                  c = b * 1
              with fluid.name_scope("s3"):
                  d = c / 1
          with fluid.name_scope("s1"):
              f = fluid.layers.pow(d, 2.0)
          with fluid.name_scope("s4"):
              g = f - 1
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    """
    # TODO(panyx0718): Only [0-9a-z].
    assert prefix, "namescope prefix cannot be empty."
    global _name_scope
    _name_scope = _name_scope.child(prefix)
    yield
    _name_scope = _name_scope.parent()


def _full_name_scope():
    global _name_scope
    scope = _name_scope
    name = ""
    while scope:
        name = scope.name() + "/" + name
        scope = scope.parent()
    return name


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

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

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

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

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

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

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


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class Variable(object):
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    """
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    In Fluid, every input and output of an operator is a variable. In most
    cases, variables are used for holding different kinds of data or training
    labels. A variable belongs to a block. All variable has its own name and
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    two variables in different blocks could have the same name.
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    There are many kinds of variables. Each kind of them has its own attributes
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    and usages. Please refer to the framework.proto for details.
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    Most of a Variable's member variables can be setted to be None. It mean
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    it is not available or will be specified later.
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    Args:
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        block(Block): The block that the variable belongs to.
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        type(core.VarDesc.VarType): Variable type. Please reference the
            framework.proto for details.
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        name(str|None): The name of the variable. If setted None, it will be
            generated automatically. Default: None
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        shape(tuple|list|None): The shape of the variable. -1 means the batch size.
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            Some kinds of variable do not contain shape, just set it to None.
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            Default: None
        dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable.
            Default: None
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        lod_level (int|None): The level of lod tensor. 0 means it is not a time
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            series data.
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            Default: None
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        capacity (int|None): The capacity of Channel variable. Ignored for other
            types. Default: None
        persistable (bool|None): True if the variable is persistable. A persistable
            variable will not be deleted after an iteration ending. Defaults: None.
        error_clip (BaseErrorClipAttr|None): The error clip attributes of the
            corresponding gradient variable. Default: None
        stop_gradient (bool): True if the variable will stop to calculate its
            gradients when backward. Default: False.
        is_data (bool): True if the variable is an input data. Default: False

    Notes:
        The constructor of Variable should not be invoked directly. Please
        use `Block.create_var` to create a variable.

    Examples:
        .. code-block:: python

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            import paddle.fluid as fluid
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            cur_program = 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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    """

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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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                 **kwargs):
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        self.block = block
        if name is None:
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            name = unique_name.generate('_generated_var')
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        if dtype is not None:
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            if not isinstance(dtype, core.VarDesc.VarType):
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                dtype = convert_np_dtype_to_dtype_(dtype)
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        if in_dygraph_mode():
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            # record vars in tracer rather than blocks
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            self._ivar = kwargs.get("ivar", None)
            if not self._ivar:
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                self._ivar = core.VarBase(
                    name, dtype if dtype else core.VarDesc.VarType.FP32,
                    list(shape) if shape else [],
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                    _current_expected_place(), stop_gradient, True
                    if persistable else False)
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            if persistable:
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                _dygraph_tracer().trace_var(name, self)
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            self.op = None
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        else:
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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))

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

            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))

            if shape is not None:
                if is_new_var:
                    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))

            if capacity is not None:
                if is_new_var:
                    self.desc.set_capacity(capacity)
                else:
                    # TODO(abhinavarora) : Compare with set capacity once,
                    # get_capacity is implemented
                    pass

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            self.block.vars[name] = self
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            self.op = None
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            self._stop_gradient = stop_gradient
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            self.is_data = is_data
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    def numpy(self):
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        new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
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        return np.array(new_ivar.value().get_tensor())
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    def backward(self, backward_strategy=None):
        from .dygraph import BackwardStrategy
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        if backward_strategy is None:
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            backward_strategy = BackwardStrategy()
            backward_strategy.sort_sum_gradient = False
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        self._ivar._run_backward(backward_strategy)
        _dygraph_tracer()._clear_ops()
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    def gradient(self):
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        new_ivar = self._ivar._grad_ivar()._copy_to(core.CPUPlace(), True)
        return np.array(new_ivar.value().get_tensor())
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    def clear_gradient(self):
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        self._ivar._clear_gradient()
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    def __str__(self):
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        return self.to_string(True)

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

        Args:
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            throw_on_error(bool): True if raise an exception when self is
                not initialized.
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            with_details(bool): more details about variables and parameters
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                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False;
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        Returns:
            str: The debug string.
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        """
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        if in_dygraph_mode():
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            # TODO(panyx0718): add more dygraph debug info.
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            return 'name %s, dtype: %s shape: %s %s' % (
                self.name, self.dtype, self.shape,
                str(self._ivar.value().get_tensor()))
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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, six.binary_type(getattr(self, attr_name)))
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        return res_str
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    __repr__ = __str__

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    def set_desc(self, input):
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        """
        Set the variable description.

        Args:
            input(core.VarDesc): The new VarDesc.

        Returns:
            None
        """
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        self.desc = input

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    @property
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    def stop_gradient(self):
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        if in_dygraph_mode():
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            return self._ivar.stop_gradient
        else:
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            return self._stop_gradient
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    @stop_gradient.setter
    def stop_gradient(self, s):
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        if in_dygraph_mode():
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            self._ivar.stop_gradient = s
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        else:
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            self._stop_gradient = s
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    @property
    def persistable(self):
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        if in_dygraph_mode():
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            return self._ivar.persistable
        else:
            return self.desc.persistable()
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    @persistable.setter
    def persistable(self, p):
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        if in_dygraph_mode():
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            return self._ivar.persistable
        else:
            self.desc.set_persistable(p)
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    @property
    def name(self):
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        if in_dygraph_mode():
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            return self._ivar.name
        else:
            return cpt.to_text(self.desc.name())
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    @name.setter
    def name(self, new_name):
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        if in_dygraph_mode():
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            self._ivar.name = new_name
        else:
            self.desc.set_name(new_name)
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    @property
    def shape(self):
        # convert to tuple, make it as same as numpy API.
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        if in_dygraph_mode():
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            return self._ivar.shape
        else:
            return tuple(self.desc.shape())
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    @property
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    def dtype(self):
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        if in_dygraph_mode():
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            return self._ivar.dtype
        else:
            return self.desc.dtype()
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    @property
    def lod_level(self):
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        # TODO(minqiyang): Support lod_level in dygraph mode
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        if in_dygraph_mode():
            raise Exception("Dygraph model 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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        if in_dygraph_mode():
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            return self._ivar.dtype
        else:
            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 _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:
            raise ValueError("slice step cannot be zero")

        # Find lower and upper bounds for start and stop.
        lower = -1 if step < 0 else 0
        upper = length - 1 if step < 0 else length

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

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

        return start, stop, step

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

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

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

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

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

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

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

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

        Returns:
            Sliced variable
        """
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        if not isinstance(item, tuple):
            item = [item]

        decrease_axis = []
        slice_axis = []
        slice_start = []
        slice_end = []
        reverse_axis = []

        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 slice_item.step else 1

                assert (step == 1 or step == -1)

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

                if start is None and end is None:
                    continue

                if start is None:
                    start = 0

                if end is None:
                    end = 10000000

                slice_axis.append(dim)
                slice_start.append(start)
                slice_end.append(end)
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            else:
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                # int
                decrease_axis.append(dim)
                slice_axis.append(dim)
                slice_start.append(slice_item)
                slice_end.append(slice_item + 1
                                 if slice_item != -1 else 10000000)

        out = self
        if len(slice_axis) > 0:
            # append slice_op here

            slice_out_var = self.block.create_var(
                name=unique_name.generate_with_ignorable_key(self.name +
                                                             "_slice"),
                dtype=self.dtype)

            self.block.append_op(
                type="slice",
                inputs={'Input': [out]},
                outputs={'Out': [slice_out_var]},
                attrs={
                    'axes': slice_axis,
                    'starts': slice_start,
                    'ends': slice_end,
                    'decrease_axis': decrease_axis
                })

            out = slice_out_var

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

            out = reverse_out_var

        return out
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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


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

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

    def __init__(self):
        assert not hasattr(
            self.__class__,
956
            '_instance'), 'Please use `instance()` to get OpProtoHolder object!'
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        op_protos = get_all_op_protos()
        self.op_proto_map = {}
        for proto in op_protos:
            self.op_proto_map[proto.type] = proto

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

        Returns(framework_pb2.OpProto): The OpProto

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

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    @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(),
            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName()
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        }

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class Operator(object):
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    """
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    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.
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        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

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            import paddle.fluid as fluid
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            cur_program = Program()
            cur_block = cur_program.current_block()
            # var1 += var2 + var3
            cur_block.append_op(type="sum",
                                inputs={"X": [var1, var2, var3]},
                                outputs={"Out": [var1]})
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    """
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    OP_WITHOUT_KERNEL_SET = {
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        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
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        'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id',
        'c_gen_nccl_id', 'c_comm_init', 'c_sync_calc_stream',
        'c_sync_comm_stream'
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    }
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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.iop = core.OpBase(type)
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            self.previous_ops = []
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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(
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                )] = self.block.program._op_role
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            role_var_name = op_maker.kOpRoleVarAttrName()
            if len(self.block.program.
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                   _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(
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                    "`type` to initialized an Operator can not be None.")
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            else:
                callstack_var_name = op_maker.kOpCreationCallstackAttrName()
                op_attrs[callstack_var_name] = list(
                    reversed(traceback.format_stack()))[1:]

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

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

            def find_name(var_list, name):
                for var_name in var_list:
                    if var_list[var_name] is not None and var_name == name:
                        return True
                return False

            if inputs is not None:
                for in_proto in proto.inputs:
                    found = find_name(inputs, in_proto.name)
                    assert found or in_proto.dispensable, "Input {} not found".format(
                        in_proto.name)
                    if found:
                        in_args = inputs[in_proto.name]
                        if not isinstance(in_args, list):
                            in_args = [in_args]
                        if not in_proto.duplicable and len(in_args) > 1:
                            raise ValueError(
                                "Input %s expects only one input, but %d are given."
                                % (in_proto.name, len(in_args)))
                        in_arg_names = []
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                        for index, arg in enumerate(in_args):
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                            if isinstance(arg, six.string_types):
                                in_arg_names.append(arg)
                            elif isinstance(arg, six.binary_type):
                                in_arg_names.append(arg.decode())
1116
                            elif isinstance(arg, Variable):
1117
                                in_arg_names.append(cpt.to_text(arg.name))
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                            else:
                                raise ValueError(
                                    "not suprt args type , should be[ string_type, binary_type, Varibale]"
                                )
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                        self.desc.set_input(in_proto.name, in_arg_names)
                    else:
                        self.desc.set_input(in_proto.name, [])

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

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

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

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

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

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        Args:
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            throw_on_error(bool): Whether to raise exception if self is not
                initialized.
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        Returns:
            str: The debug string.
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        """
1183
        protostr = self.desc.serialize_to_string()
1184
        proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
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        return _debug_string_(proto, throw_on_error)

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

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    @property
    def type(self):
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        if in_dygraph_mode():
1195 1196 1197
            return self.iop.type
        else:
            return self.desc.type()
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    def input(self, name):
1200
        """
1201
        Get the input arguments according to the input parameter name.
1202

1203 1204
        Args:
            name(str): The input parameter name.
1205

1206 1207 1208
        Returns:
            list: return the list of argument names that associated with \
                the specific parameter name.
1209
        """
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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):
1251
        """
1252
        Get output arguments by the output parameter name.
1253

1254 1255
        Args:
            name(str): The output parameter name.
1256

1257 1258 1259
        Returns:
            list: return the list of argument names associated with \
                the specific parameter name.
1260
        """
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        return self.desc.output(name)

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

1267 1268 1269 1270 1271 1272 1273 1274
    @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):
1276
        """
1277 1278
        Whether this Operator has the attribute with name or not.

1279
        Args:
1280
            name(str): the attribute name.
1281

1282 1283
        Returns:
            bool: True if has this attribute.
1284 1285

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

    def attr_type(self, name):
1289
        """
1290
        Get the type of attribute by attribute's name.
1291

1292 1293
        Args:
            name(str): the attribute name.
1294

1295 1296
        Returns:
            core.AttrType: the attribute type.
1297
        """
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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):
1331
            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):
1343
        """
1344 1345
        Get the attribute by name.

1346
        Args:
1347
            name(str): the attribute name.
1348

1349 1350
        Returns:
            bool|int|str|float|list: The attribute value. The return value
1351 1352
            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):
1356
        """
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        Get the block attribute's id by name.
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1359 1360
        Args:
            name(str): the attribute name.
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        Returns:
            int: the block index.
1364
        """
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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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        """
1414 1415 1416
        Get the attribute dict.

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

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

            attr_map[n] = self.attr(n)

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        return attr_map

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

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

    Notes:
        The constructor of Block should not be invoked directly. Please
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        use `Program._create_block()` to create a block.
1452 1453 1454 1455

    Examples:
        .. code-block:: python

1456 1457 1458
            import paddle.fluid as fluid

            cur_program = fluid.Program()
1459 1460 1461 1462 1463 1464 1465 1466 1467
            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)
1470
        self.vars = collections.OrderedDict()  # var_name --> var
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        self.ops = list()  # operator list
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        self.program = program
1473
        self.removed_vars = collections.OrderedDict()
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1475
    def __str__(self):
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        return self.to_string(True)

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    def to_string(self, throw_on_error, with_details=False):
        """
1480 1481
        Get debug string.

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        Args:
            throw_on_error(bool): raise exception when self is not initialized
1484
                when throw_on_error is True.
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            with_details(bool): more details about variables and parameters
1486 1487
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False.
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1489 1490
        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)
1498
            for var in list(self.vars.values()):
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                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
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                    r"\n    \1", var.to_string(throw_on_error, with_details))
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            for op in self.ops:
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                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                    r"\n    \1", op.to_string(throw_on_error))
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            res_str += "\n}"
        else:
            protostr = self.desc.serialize_to_string()
1507 1508
            proto = framework_pb2.BlockDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
1511 1512 1513

    __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):
1523 1524 1525 1526 1527 1528 1529 1530 1531
        """
        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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    @property
    def idx(self):
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        return self.desc.id
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    def var(self, name):
1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551
        """
        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.
        """
1552
        if not isinstance(name, six.string_types):
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            raise TypeError(
                "var require string as parameter, but get %s instead." %
                (type(name)))
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        v = self.vars.get(name, None)
        if v is None:
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            raise ValueError("var %s not in this block" % name)
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        return v
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    def _find_var_recursive(self, name):
1562 1563 1564 1565 1566 1567 1568
        """
        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.
1570
        """
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        frontier = list()
        visited = set()

        frontier.append(self)

        prog = self.program

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

            if id(cur) in visited:
                continue

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

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

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

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

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

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

        Returns:
            Variable: the Variable with the giving name.
        """
        var = self._find_var_recursive(name)
        if var:
            return var
        else:
            raise ValueError("Var {0} is not found recursively".format(name))
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1617
    def all_parameters(self):
1618
        return list(self.iter_parameters())
1619

1620
    def iter_parameters(self):
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        return (item[1] for item in six.iteritems(self.vars)
1622
                if isinstance(item[1], Parameter))
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1623

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    def create_var(self, *args, **kwargs):
1625
        var = Variable(block=self, *args, **kwargs)
1626 1627
        if 'initializer' in kwargs:
            kwargs['initializer'](var, self)
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        return var
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    def has_var(self, name):
        return name in self.vars

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    def _rename_var(self, name, new_name):
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        """
        Rename variable in vars and ops' inputs and outputs
1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647

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

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

        Returns:
            Variable: the Variable with the giving name.
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        """
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        name = cpt.to_text(name)
        new_name = cpt.to_text(new_name)
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        if not self.has_var(name):
1653
            raise ValueError("var %s is not in current block" % name)
T
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        v = self.var(name)
        if type(v) == Parameter:
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            var_type = "Parameter"
T
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1657 1658 1659 1660 1661 1662 1663
            stop_gradient = v.stop_gradient
            trainable = v.trainable
            optimize_attr = v.optimize_attr
            regularizer = v.regularizer
            gradient_clip_attr = v.gradient_clip_attr
            error_clip = v.error_clip
        elif type(v) == Variable:
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            var_type = "Variable"
T
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            error_clip = v.error_clip
            stop_gradient = v.stop_gradient
        else:
            raise ValueError("unsupported var type: %s", type(v))
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        orig_var_type = v.type
M
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        self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
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        # NOTE: v is destroyed by C++ after calling _rename_var.
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        d = self.desc.find_var(cpt.to_bytes(new_name))
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        if var_type == "Parameter":
T
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1674 1675 1676 1677
            var = Parameter(
                self,
                d.shape(),
                d.dtype(),
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                type=orig_var_type,
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                name=new_name,
                stop_gradient=stop_gradient,
                trainable=trainable,
                optimize_attr=optimize_attr,
                regularizer=regularizer,
                gradient_clip_attr=gradient_clip_attr,
                error_clip=error_clip)
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        elif var_type == "Variable":
T
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1687 1688
            var = Variable(
                self,
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                type=orig_var_type,
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1690 1691 1692 1693
                name=new_name,
                error_clip=error_clip,
                stop_gradient=stop_gradient)

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        # rename the python side, _sync_with_cpp will only add
T
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1695 1696 1697
        # new vars/ops to python side.
        self.vars[new_name] = var
        del self.vars[name]
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        self._sync_with_cpp()
1699
        return var
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    def _remove_var(self, name):
        self._sync_with_cpp()
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        self.desc._remove_var(cpt.to_bytes(name))
1704 1705
        del self.vars[name]

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    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
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        param = Parameter(global_block, *args, **kwargs)
1709
        if 'initializer' in kwargs:
1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729

            def _is_inited_by(block, var):
                init_ops = []
                for op in block.ops:
                    if var.name in op.output_arg_names:
                        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:
                #TODO already inited, do nothing, should log a warning
                pass
            else:
                initializer(param, self)
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        return param
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    def append_op(self, *args, **kwargs):
1733 1734 1735 1736 1737 1738
        """
        Appends a new Operator according to the giving arguments.

        Returns:
            Operator: the append Operator.
        """
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        if in_dygraph_mode():
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            attrs = kwargs.get("attrs", {})
            if _dygraph_tracer_._train_mode == False:
                # eval mode
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                if ('trainable_statistics' not in attrs
                    ) or not attrs['trainable_statistics']:
                    attrs['is_test'] = True
                else:
                    attrs['is_test'] = False
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            op = Operator(
                block=self,
                desc=None,
                type=kwargs.get("type", None),
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                inputs=None,
                outputs=None,
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                attrs=attrs)
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            # record ops in tracer rather than blocks
            #
            # TODO(minqiyang): add op stop_gradient support in static mode too.
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            # currently, we only support stop_gradient in dygraph mode.
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            _dygraph_tracer().trace_op(op,
                                       kwargs.get("inputs", {}),
                                       kwargs.get("outputs", {}),
                                       kwargs.get("stop_gradient", False))
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        else:
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            op_desc = self.desc.append_op()
            op = Operator(
                block=self,
                desc=op_desc,
                type=kwargs.get("type", None),
                inputs=kwargs.get("inputs", None),
                outputs=kwargs.get("outputs", None),
                attrs=kwargs.get("attrs", None))

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            self.ops.append(op)
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        return op

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    def _insert_op(self, index, *args, **kwargs):
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        """
        Insert a Operator according to the giving arguments.

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

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

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    def _remove_op(self, index):
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        """
        Remove the specific position operator.

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

        Returns:
            None
        """
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        self._sync_with_cpp()
        self.desc._remove_op(index, index + 1)
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        del self.ops[index]

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    def _slice_ops(self, start, end):
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        """
        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.
        """
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        return self.ops[start:end]
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    def _prepend_op(self, *args, **kwargs):
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        if in_dygraph_mode():
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            op = Operator(
                self,
                None,
                type=kwargs.get("type", None),
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                inputs=None,
                outputs=None,
                attrs=kwargs.get("attrs", {}))

            _dygraph_tracer().trace_op(op,
                                       kwargs.get("inputs", {}),
                                       kwargs.get("outputs", {}),
                                       kwargs.get("stop_gradient", False))
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        else:
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            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)
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        return op

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    def _sync_with_cpp(self):
1850
        """
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        Sync from the desc on the c++ end. This method is used to synchronize
        the c++ desc instance generated by backward.
1853
        """
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        # 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())

1859
        # sync variables removed from c++ end
1860
        for var in list(self.vars.keys()):
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            if not self.desc.find_var(cpt.to_bytes(var)):
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                self.vars.pop(var)

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        # sync operators from cpp
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        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

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        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
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        # 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)
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            self.ops.insert(0, op)
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        # 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)

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        # 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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        assert len(self.ops) == len(ops_in_cpp)
        for index in range(len(self.ops)):
            assert self.ops[index].desc == ops_in_cpp[index]

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    def _copy_param_info_from(self, other):
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        """
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        Copy the information of parameters from the other block.

1919
        Args:
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            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.
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        Returns:
            None
        """
        if not isinstance(other, Block):
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            raise TypeError(
                "_copy_param_info_from should be invoked with Block")
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        for p in other.iter_parameters():
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            assert isinstance(p, Parameter)
            v = self.vars.get(p.name, None)
            if v is None:
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                raise ValueError("_copy_param_info_from should be invoked with "
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                                 "same topology")
            assert isinstance(v, Variable)
            new_p = Parameter(
                block=self,
                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,
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                gradient_clip_attr=p.gradient_clip_attr,
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                error_clip=p.error_clip,
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                name=v.name)
            self.vars[new_p.name] = new_p

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    def _clone_variable(self, var, force_persistable=True):
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        """
        Clone a variable into current block.
1957

1958 1959
        Args:
            var: the variable to be cloned.
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            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.
1963 1964

        Returns:
1965
            Variable: the new  variable cloned from 'var' in current block.
1966 1967
        """
        assert isinstance(var, Variable)
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        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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        elif var.type == core.VarDesc.VarType.RAW:
            ret_var = self.create_var(
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                name=var.name, persistable=var.persistable, type=var.type)
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        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,
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                persistable=True if force_persistable else var.persistable,
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                is_data=var.is_data)
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        else:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
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                persistable=True if force_persistable else var.persistable,
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                is_data=var.is_data)
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        return ret_var
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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()

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    def remove_input_by_id(self, node_id):
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        """
        Remove a node from inputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
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        self.node.remove_input(node_id)
2099

2100
    def remove_input(self, node):
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        """
        Remove a node from inputs.

        Args:
2105
            node(IrNode): the node being removed.
2106
        """
2107
        self.node.remove_input(node.node)
2108

2109
    def append_input(self, node):
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        """
        Append a node in inputs.

        Args:
2114
            node(IrNode): the node being appended.
2115
        """
2116
        self.node.append_input(node.node)
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    def clear_outputs(self):
        """
        Clear the node outputs. After executing the `clear_outputs` function,
        the node outputs will be empty.
        """
        self.node.clear_outputs()

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    def remove_output_by_id(self, node_id):
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        """
        Remove a node from outputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
2132
        self.node.remove_output(node_id)
2133

2134
    def remove_output(self, node):
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        """
        Remove a node from outputs.

        Args:
2139
            node(IrNode): the node being removed.
2140
        """
2141
        self.node.remove_output(node.node)
2142

2143
    def append_output(self, node):
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        """
        Append a node in outputs.

        Args:
2148
            node(IrNode): the node being appended.
2149
        """
2150
        self.node.append_output(node.node)
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    @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, \
            "The node variable description cannot be None."
        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, \
            "The node variable description cannot be None."
        return self.node.var().persistable()

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

        Returns:
            core.VarDesc.VarType: the variable type.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        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, \
            "The node variable description cannot be None."
        return self.node.var().dtype()

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

        Returns:
            list: the variable shape.
        """
        assert self.node.var() is not None, \
            "The node variable description cannot be None."
        return self.node.var().shape()

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    @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, \
            "The node operator description cannot be None."
        self.node.op()._rename_input(old_input_name, new_input_name)

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    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, \
            "The node operator description cannot be None."
        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, \
            "The node operator description cannot be None."
        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, \
            "The node operator description cannot be None."
        return self.node.op().set_type(new_type)

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    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, \
            "The node operator description cannot be None."
        desc = self.node.op()
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and \
            all(isinstance(v, Block) for v in val):
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
            isinstance(val, core.ProgramDesc):
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)

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    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, \
            "The node operator description cannot be None."
        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, \
            "The node operator description cannot be None."
        return self.node.op().output_arg_names()

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    @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]


2405 2406
class IrGraph(object):
    """
2407
    Python IrGraph. Beneath it is a core.Graph, which is used for
2408
    creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
2409 2410
    a Program. In an IrGraph, both Variables and Operators are graph
    nodes.
2411 2412 2413 2414
    """

    def __init__(self, graph, for_test=False):
        """
2415 2416
        Construct an IrGraph using core.Graph.

2417 2418 2419 2420 2421 2422 2423 2424 2425
        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

2426 2427 2428 2429
    def clone(self):
        """
        Create a new and duplicated IrGraph.

2430 2431 2432
        Warns:
            The method only clones the graph structure, not its attributes.

2433 2434 2435
        Returns:
            IrGraph: A new and duplicated graph.
        """
2436
        g = self.graph.clone()
2437 2438
        return IrGraph(g, self._for_test)

2439
    def is_test(self):
2440 2441 2442
        """
        If the graph is used for testing, the function returns true. Otherwise, returns false.
        """
2443 2444
        return self._for_test

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    def all_nodes(self):
2446 2447 2448
        """
        Return all nodes included in the graph as a set.
        """
2449
        return {IrNode(node) for node in self.graph.nodes()}
2450

2451
    def all_var_nodes(self):
2452 2453 2454
        """
        Return all variable nodes included in the graph as a set.
        """
2455
        return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
2456

2457
    def all_persistable_nodes(self):
2458 2459 2460
        """
        Return all persistable variable nodes included in the graph as a set.
        """
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        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)
2466
        return {IrVarNode(p) for p in persistable_nodes}
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2468
    def all_op_nodes(self):
2469 2470 2471
        """
        Return all operator nodes included in the graph as a set.
        """
2472
        return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
2473

2474
    def create_persistable_node(self, name, var_type, shape, var_dtype):
2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485
        """
        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:
2486
            IrVarNode: the created persistable variable node.
2487
        """
2488 2489 2490 2491 2492
        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)
2493
        return IrVarNode(self.graph.create_var_node(var_desc))
2494 2495

    def create_var_node(self, name, var_type, shape, var_dtype):
2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506
        """
        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:
2507
            IrVarNode: the created variable node.
2508 2509
        """

2510 2511 2512 2513
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
2514
        return IrVarNode(self.graph.create_var_node(var_desc))
2515 2516

    def create_var_node_from_desc(self, var_desc):
2517 2518 2519 2520 2521 2522 2523 2524
        """
        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:
2525
            IrVarNode: the created variable node.
2526
        """
2527
        return IrVarNode(self.graph.create_var_node(var_desc))
2528 2529

    def create_op_node(self, op_type, attrs, inputs, outputs):
2530 2531 2532 2533 2534 2535 2536 2537 2538 2539
        """
        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.
            outputs(dict): the outpus of the operator node.

        Returns:
2540
            IrOpNode: the created operator node.
2541
        """
2542 2543
        op_desc = core.OpDesc()
        op_desc.set_type(op_type)
2544
        for attr, value in six.iteritems(attrs):
2545
            self._update_desc_attr(op_desc, attr, value)
2546
        for input_name, var_nodes in six.iteritems(inputs):
2547 2548 2549 2550
            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])
2551
        for output_name, var_nodes in six.iteritems(outputs):
2552 2553 2554 2555
            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])
2556
        return IrOpNode(self.graph.create_op_node(op_desc))
2557 2558

    def create_op_node_from_desc(self, op_desc):
2559 2560 2561 2562 2563 2564 2565
        """
        Create a operator node by using an existing OpDesc in the graph.

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

        Returns:
2566
            IrOpNode: the created operator node.
2567
        """
2568
        return IrOpNode(self.graph.create_op_node(op_desc))
2569 2570

    def update_input_link(self, old_input_node, new_input_node, op_node):
2571 2572 2573 2574
        """
        Update the input's link of a operator node.

        Args:
2575 2576 2577
            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.
2578
        """
2579 2580
        assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
        self.graph.nodes() and op_node.node in self.graph.nodes(), \
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        'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
2582 2583 2584 2585
        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)
2586
        op_node.rename_input(old_input_node.name(), new_input_node.name())
2587 2588

    def link_to(self, node_in, node_out):
2589 2590 2591 2592
        """
        Connect two nodes.

        Args:
2593 2594
            node_in(IrNode): the input node.
            node_out(IrNode): the output node.
2595
        """
2596
        assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \
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            'The two arguments(node_in&node_out) must be in the graph nodes.'
2598 2599
        node_in.append_output(node_out)
        node_out.append_input(node_in)
2600 2601

    def safe_remove_nodes(self, remove_nodes):
2602 2603 2604 2605 2606 2607 2608
        """
        Remove nodes safely since links connected to these removed nodes are
        also removed.

        Args:
            remove_nodes(set): the nodes prepared to be removed.
        """
2609
        if not isinstance(remove_nodes, set):
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            if isinstance(remove_nodes, Iterable):
                remove_nodes = set(remove_nodes)
            else:
                remove_nodes = {remove_nodes}
2614 2615
        original_nodes = {n.node for n in remove_nodes}
        core.graph_safe_remove_nodes(self.graph, original_nodes)
2616

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    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] = [
2625
                            self._find_node_by_name(node.inputs, each_var_name)
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                        ]
                for each_var_name in node.op().output_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
2630
                            self._find_node_by_name(node.outputs, each_var_name)
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                        ]
                    else:
                        var_nodes[each_var_name].append(
2634 2635
                            self._find_node_by_name(node.outputs,
                                                    each_var_name))
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        self.graph.resolve_hazard(var_nodes)

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    def has_circle(self):
2639 2640 2641 2642 2643 2644
        """
        Check if the graph has a circle.

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

    def graph_num(self):
2648 2649 2650 2651 2652 2653
        """
        Count the number of unconnected graphs in this graph.

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

    def topology_sort(self):
2657 2658 2659 2660 2661 2662
        """
        Perform the topology sort operation on the graph.

        Notes: the `graph` cannot contain a circle.

        Returns:
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            list(IrNode): nodes in topology order.
2664
        """
2665
        ordered_nodes = core.topology_sort(self.graph)
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        return [IrNode(n) for n in ordered_nodes]
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    def build_adjacency_list(self):
2669 2670 2671 2672
        """
        Build an adjacency list of operations for the `graph`.

        Returns:
2673
            dict{IrNode: set(IrNode)}: the adjacency list.
2674
        """
2675 2676 2677 2678 2679
        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
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2681 2682 2683 2684 2685 2686 2687 2688
    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.
2689
            marked_nodes(set(IrNode)): nodes that are needed to be marked.
2690 2691 2692 2693 2694
            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.
        """

2695 2696 2697 2698 2699 2700 2701 2702 2703
        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 \
                            + ' -o ' + pdf_save_path, shell=True)
            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))

2704
        remove_ctr_vars = set()
2705
        if remove_ctr_var:
2706
            for node in self.all_var_nodes():
2707 2708 2709
                if node.is_ctrl_var():
                    remove_ctr_vars.add(node)
            self.safe_remove_nodes(remove_ctr_vars)
2710 2711
        print('Total ops num = {}.'.format(len(self.all_op_nodes())))

2712 2713
        if marked_nodes is not None:
            if not isinstance(marked_nodes, set):
2714 2715 2716 2717 2718 2719
                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}
2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730
            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)
        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):
2731 2732 2733
        """
        Convert the graph into a Program.

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        WARN: When the graph includes backward operator nodes, the
2735 2736 2737 2738 2739 2740
        conversion process may be failed. Usually, this function is
        only used to convert a test graph.

        Returns:
            Program: a program converted from the graph.
        """
2741
        convert_pass = core.get_pass('graph_to_program_pass')
2742 2743
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
2744 2745 2746 2747
        convert_pass.apply(self.graph)
        program = Program._construct_from_desc(desc)
        return program

2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758
    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

2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774
    def _update_desc_attr(self, desc, name, val):
        """
        Update the value of desc's attribute by attribute's name.
        """
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and all(
                isinstance(v, Block) for v in val):
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
                isinstance(val, core.ProgramDesc):
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)


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class Program(object):
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    """
    Python Program. Beneath it is a ProgramDesc, which is used for
    create c++ Program. A program is a self-contained programing
    language like container. It has at least one Block, when the
    control flow op like conditional_block, while_op is included,
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    it will contain nested block.
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    Please reference the framework.proto for details.

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    A set of Program usually contains startup program and main program.
    A startup program is set to contain some initial work , and the main
    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.

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    Notes: we have default_startup_program and default_main_program
    by default, a pair of them will shared the parameters.
    The default_startup_program only run once to initialize parameters,
Y
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    default_main_program run in every mini batch and adjust the weights.
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2797 2798

    Returns:
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        A empty program.
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2800 2801

    Examples:
2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814
        .. code-block:: python

            import paddle.fluid as fluid

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

            print("main program is: {}".format(main_program))
            print("start up program is: {}".format(startup_program))
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2815 2816 2817

    """

2818 2819
    def __init__(self):
        self.desc = core.ProgramDesc()
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        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0
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        self._seed = 0
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        self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
2824
        self.__op_role_var = []
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2825

2826 2827
        # for distribute training
        # _is_distributed = True if under distributed training
T
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        self._is_distributed = False
2829
        # _is_chief = True if the trainer is the first one, usually No.0
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        self._is_chief = False
2831 2832 2833
        # _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
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        self._endpoints = []
2835 2836 2837
        # if current role is parameter server, the _ps_endpoint is its "ip:port"
        self._ps_endpoint = None
        # trainers_endpoints, it is used for distribution.
2838
        self._trainers_endpoints = []
2839
        # the distributed lookup table names
T
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        self._distributed_lookup_table = None
2841 2842 2843

        # use Deep gradient comrepssion or not
        self._enable_dgc = False
2844 2845 2846
        self._nccl_comm_num = 1
        self._use_hierarchical_allreduce = False
        self._hierarchical_allreduce_inter_nranks = 0
2847

D
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        # @deprecated(the python memory optimize transpiler is deprecated)
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        # whether the program is optimized by memory_optimize_transpiler
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        self.__is_mem_optimized = False
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2852 2853 2854
        # if this program has been optimized by distributed optimizer
        # fleet_opt will be given a value
        self._fleet_opt = None
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        self._program_config = None
2856

H
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2857 2858 2859
        # assigned if this program has been parsed by a pipeline optimizer
        self._pipeline_opt = None

2860 2861 2862
        # appending gradients times
        self._appending_grad_times = 0

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    @property
D
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    def _is_mem_optimized(self):
D
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2865 2866
        # if the program is optimized, operator input/outputs
        # maybe same, which conflict with save_inference_model.
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        return self.__is_mem_optimized
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2869 2870 2871
    @_is_mem_optimized.setter
    def _is_mem_optimized(self, target):
        self.__is_mem_optimized = target
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    @property
2874
    def _op_role(self):
Y
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2875 2876 2877 2878 2879 2880 2881 2882
        """
        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
2883
        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

2890 2891
    @_op_role.setter
    def _op_role(self, role):
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2892 2893 2894
        self._current_role = role

    @property
2895
    def _op_role_var(self):
Y
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2896
        """
2897
        The auxiliary variables for :code:`_op_role` property.
Y
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2898

2899
        See Also: :code:`Program._op_role`'s documentation for details.
Y
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        Notes: This is a very low-level API. Users should not use it directly.
        """
2903
        return self.__op_role_var
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2905 2906 2907 2908 2909 2910 2911 2912 2913
    @contextlib.contextmanager
    def _backward_role_guard(self):
        tmp_role = self._current_role

        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Backward
        yield
        self._current_role = tmp_role

S
rename  
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    @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:
2923
            param_and_grads(list): The variables (names) to be optimized.
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2924 2925 2926

        Examples:

2927
            >>> import paddle.fluid as fluid
Y
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2928
            >>> p, g = backward(...)
W
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            >>> with program._optimized_guard([p,g]):
Y
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2930 2931
            >>>     p = p - 0.001 * g
        """
X
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2932
        tmp_role = self._current_role
2933
        tmp_var = self.__op_role_var
X
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Y
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2935 2936
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Optimize
2937
        self.__op_role_var = [
2938 2939 2940
            var.name if isinstance(var, Variable) else var
            for var in param_and_grads
        ]
Y
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        yield
2942
        self.__op_role_var = tmp_var
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        self._current_role = tmp_role
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S
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    @signature_safe_contextmanager
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    def _lr_schedule_guard(self, is_with_opt=False):
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        """
        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.
2958 2959 2960

        Examples:

2961
            >>> import paddle.fluid as fluid
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            >>> p, g = backward(...)
            >>> with program.lr_schedule_guard():
            >>>     lr = lr * decay
        """
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        tmp_role = self._current_role
2968
        tmp_var = self.__op_role_var
2969

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        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)
2974
        # TODO(typhoonzero): how to set target learning rate var
2975
        self.__op_role_var = []
2976
        yield
2977
        self.__op_role_var = tmp_var
2978
        self._current_role = tmp_role
2979

2980
    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.
        """
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        return self.to_string(True)

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    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
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        Args:
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            throw_on_error(bool): raise Value error when any of required fields
                is not set.
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            with_details(bool): True if more details about variables and
                parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need
                to print.

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        Returns:
            str : The debug string.
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        Raises:
            ValueError: If any of required fields is not set and throw_on_error is
                True.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                prog_string = prog.to_string(throw_on_error=True, with_details=False)
                print(prog_string)

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        """
        assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                               bool)
        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()
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            proto = framework_pb2.ProgramDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
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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.
        """
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        return self.desc

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

3047
    def clone(self, for_test=False):
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        """
        Create a new, duplicated program.

3051

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        Some operators, e.g., :code:`batch_norm`, behave differently between
        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`.
3056

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        * Set for_test to False when we want to clone the program for training.
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        * Set for_test to True when we want to clone the program for testing.
          We will not do any prune on program here, So if you just want an
          forward program for testing, please use :code:`clone` before using
          :code:`Opimizer.minimize`
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        Notes: 
        1. :code:`Program.clone()` method DOES NOT clone :code:`py_reader`.
        2. This API DOES NOT prune any operator. Use
        :code:`clone(for_test=True)` before backward and optimization please. E.g.
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        .. code-block:: python

            test_program = fluid.default_main_program().clone(for_test=True)
            optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
            optimizer.minimize()
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        Args:
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            for_test(bool): True if change the :code:`is_test` attribute of
                operators to :code:`True`.
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        Returns:
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            Program: The new, duplicated Program object.

        Examples:

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        Notes: The Program Descs' 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`:

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            .. code-block:: python

                import paddle.fluid as fluid
                import six


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


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

                    import paddle.fluid as fluid
                    import six

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

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

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

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                    with fluid.program_guard(train_program, startup_program):
                        with fluid.unique_name.guard():
                            sgd = fluid.optimizer.SGD(learning_rate=1e-3)
                            sgd.minimize(avg_loss)


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

                    import paddle.fluid as fluid
                    import six

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


                    train_program_2 = fluid.Program()
                    startup_program_2 = fluid.Program()
                    test_program_2 = fluid.Program()
                    with fluid.program_guard(train_program_2, startup_program_2):
                        with fluid.unique_name.guard():
                             sgd = fluid.optimizer.SGD(learning_rate=1e-3)
                             sgd.minimize(avg_loss)
                    # the test startup program is not used.
                    with fluid.program_guard(test_program_2, fluid.Program()):
                        with fluid.unique_name.guard():
                            loss = network(is_test=True)
                    print(test_program_2)

        The two code snippets above will generate and print same programs.
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        """
        if for_test:
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            p = self._inference_optimize(prune_read_op=False)
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        else:
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            p = Program()
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            p.current_block_idx = self.current_block_idx
            p._seed = self._seed
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            p.desc = core.ProgramDesc(self.desc)
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            p.blocks = [
                Block(p, i) for i in six.moves.range(self.desc.num_blocks())
            ]
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            p._current_role = self._current_role
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            p.__op_role_var = self.__op_role_var
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            p._appending_grad_times = self._appending_grad_times
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            p._sync_with_cpp()
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        p._copy_param_info_from(self)
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        p._copy_data_info_from(self)
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        p._copy_dist_param_info_from(self)
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        return p
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    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:
            targets(list|Variable|Operator): A list of variables or operators
                need to be pruned

        Returns:
            Program:  A new, pruned program.

        """
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        if not isinstance(targets, list):
            targets = [targets]
        targets_idx = []
        for t in targets:
            if not isinstance(t, Operator):
                if isinstance(t, Variable):
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                    # After transpiler processing, the op that output this
                    # variable maybe has been changed, so t.op is not reliable
3243
                    # and we need to find the current op that generate this
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                    # variable here.
                    t.op = None
                    global_block = self.global_block()
                    for idx, op in enumerate(global_block.ops):
                        if t.name in op.output_arg_names:
                            t.op = op
                            break

3252
                    t = t.op
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                    if t is None:
                        raise ValueError(
                            "The target variable must have an "
                            "associated operator that generates it.")
3257
                else:
3258 3259
                    raise ValueError("All targets of prune() can only be "
                                     "Variable or Operator.")
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            targets_idx.append([t.block.idx, t.idx])
        res = Program()
        res.desc = core.prune(self.desc, targets_idx)
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        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
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        res._sync_with_cpp()
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        return res

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

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

3277
        3. change the :code:`is_test`
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        attribute of operators to :code:`True`. All the :code:`Parameter`
        information will be lost.

3281
        Args:
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            prune_read_op(bool): remove the read ops that are added by py_reader
                                 for cpp inference library
3284

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

        Returns:
            Program: The new program.
        """
3291
        res = Program()
3292
        res.desc = core.ProgramDesc(self.desc)
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        # remove all readers and the read_op if exist
        read_op_idx = 0
        root_block = res.desc.block(0)
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        if prune_read_op:
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            while True:
                if read_op_idx >= root_block.op_size() or root_block.op(
                        read_op_idx).type() == 'read':
                    break
                read_op_idx += 1
            if read_op_idx < root_block.op_size():
                root_block._remove_op(0, read_op_idx + 1)
            for var in root_block.all_vars():
                if var.type() == core.VarDesc.VarType.READER:
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                    root_block._remove_var(cpt.to_bytes(var.name()))
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        # change all `is_test` attributes to True
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        for i in six.moves.range(res.desc.num_blocks()):
3311
            block = res.desc.block(i)
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            for j in six.moves.range(block.op_size()):
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                op = block.op(j)
                if op.has_attr('is_test'):
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                    op._set_attr('is_test', True)
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        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
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        res._sync_with_cpp()
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        return res

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    @staticmethod
    def parse_from_string(binary_str):
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        """
        Deserialize a program desc from protobuf binary string.

        Notes: All information about parameters will be lost after serialization
        and deserialization.

        Args:
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            binary_str_type(str): The binary prootbuf string.
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        Returns:
            Program: A deserialized program desc.
        """
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        p = Program()
        p.desc = core.ProgramDesc(binary_str)
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        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
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        p._sync_with_cpp()
3340
        return p
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3342
    @staticmethod
3343
    def _construct_from_desc(desc):
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        """
        Construct a program from program desc.

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

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

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    @property
    def random_seed(self):
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        """
        The default random seed for random operators in Program. Zero means get
        the random seed from random device.

        Notes: It must be set before the operators have been added.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                random_seed = prog.random_seed
                print(random_seed)
                prog.random_seed = 1
                print(prog.random_seed)
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        """
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        return self._seed

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    @property
    def num_blocks(self):
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        """
        The number of blocks in this program.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                num_blocks = prog.num_blocks
                print(num_blocks)
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        """
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        return self.desc.num_blocks()

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    @random_seed.setter
    def random_seed(self, seed):
        if not isinstance(seed, int):
            raise ValueError("Seed must be a integer.")
        self._seed = seed

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    def __repr__(self):
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        return self.__str__()
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    def global_block(self):
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        """
        Get the first block of this program.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                gb_block = prog.global_block()
                print(gb_block)
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        """
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        return self.blocks[0]

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    def block(self, index):
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        """
        Get the :code:`index` block of this program
        Args:
            index(int): The index of block to get

        Returns:
            Block: The :code:`index` block
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

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

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    def current_block(self):
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        """
        Get the current block. The :code:`current` block is the block to append
        operators.
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        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

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

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

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

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

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

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

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

        Returns:
            None
        """
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        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
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            block._sync_with_cpp()
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    def _copy_param_info_from(self, other):
3500
        """
3501
        Copy the information of parameters from other program.
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        Notes: This is a very low level API. Users should not invoke it
        directly.

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        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
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            raise TypeError("_copy_param_info_from should be invoked with "
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                            "Program")

        if len(self.blocks) != len(other.blocks):
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            raise ValueError("_copy_param_info_from should be invoked with two "
3518
                             "program, with represent the same topology")
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        self.global_block()._copy_param_info_from(other.global_block())
3520

3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535
    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):
            raise TypeError("_copy_dist_param_info_from should be invoked with "
                            "Program")
        self._is_distributed = other._is_distributed
        self._is_chief = other._is_chief
3536
        self._parameters_on_pservers = other._parameters_on_pservers
3537
        self._endpoints = other._endpoints
3538
        self._ps_endpoint = other._ps_endpoint
3539 3540
        self._distributed_lookup_table = other._distributed_lookup_table

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    def _copy_data_info_from(self, other):
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        """
        Copy the information of data variables from other program.
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        Notes: This is a very low level API. Users should not invoke it
        directly.

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        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
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            raise TypeError("_copy_param_info_from should be invoked with "
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                            "Program")

        if len(self.blocks) != len(other.blocks):
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            raise ValueError("_copy_param_info_from should be invoked with two "
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                             "program, with represent the same topology")
3561
        for var in list(other.global_block().vars.values()):
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            if var.is_data:
                self.global_block().var(var.name).is_data = True

3565
    def list_vars(self):
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        """
        Get all variables from this Program. A iterable object is returned.

        Returns:
            iterable: The generator will yield every variable in this program.
3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

                prog = fluid.default_main_program()
                img = fluid.layers.data(name='img', shape=[1,28,28], dtype='float32')
                label = fluid.layers.data(name='label', shape=[128,1], dtype='int64')
                for var in prog.list_vars():
                    print(var)
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        """
3583
        for each_block in self.blocks:
3584
            for each_var in list(each_block.vars.values()):
3585 3586
                yield each_var

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class Parameter(Variable):
3589
    """
3590
    Parameter is derived from Variable. A parameter is a persistable
3591
    Variable, and will be updated by optimizers after each iteration.
3592
    The training of a neural network is essentially the updating of
3593 3594
    its parameters.

3595
    Relative to a general Variable, a Parameter has several its own
3596 3597
    member variables:

3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609
    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
        gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy
            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.
3610 3611
    """

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    def __init__(self, block, shape, dtype, **kwargs):
        if shape is None or dtype is None:
            raise ValueError("Parameter must set shape and dtype")
        if len(shape) == 0:
            raise ValueError("Parameter shape cannot be empty")

        for each in shape:
            if each < 0:
                raise ValueError("Parameter shape should not be related with "
                                 "batch-size")
3622 3623 3624

        Variable.__init__(
            self, block, persistable=True, shape=shape, dtype=dtype, **kwargs)
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        self.trainable = kwargs.get('trainable', True)

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

3629 3630
        self.regularizer = kwargs.get('regularizer', None)

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        self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
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        self.do_model_average = kwargs.get('do_model_average', None)
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3635 3636
        self.is_distributed = False

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

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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 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.

3652 3653 3654 3655 3656 3657 3658 3659 3660
        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)
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        """
        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",
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                               "gradient_clip_attr", "do_model_average")
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            for attr_name in additional_attr:
3669 3670
                res_str += "%s: %s\n" % (
                    attr_name, six.binary_type(getattr(self, attr_name)))
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        else:
            res_str = Variable.to_string(self, throw_on_error, False)
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        return res_str

    __repr__ = __str__

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# program is a global instance.
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_main_program_ = Program()
_startup_program_ = Program()
3681

3682

3683
def default_startup_program():
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    """
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    Get default/global startup program.

    The layer function in :code:`fluid.layers` will create parameters, readers,
    NCCL handles as global variables. The :code:`startup_program` will
    initialize them by the operators in startup program. The layer function will
    append these initialization operators into startup program.

    This method will return the :code:`default` or the :code:`current` startup
    program. Users can use :code:`fluid.program_guard` to switch program.
3694

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    Returns:
        Program: startup program
3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

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

                print("main program is: {}".format(fluid.default_main_program()))
                print("start up program is: {}".format(fluid.default_startup_program()))
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    """
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    return _startup_program_
3714

3715

3716
def default_main_program():
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    """
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    Get default/global main program. The main program is used for training or
    testing.

    All layer function in :code:`fluid.layers` will append operators and
    variables to the :code:`default_main_program`.

    The :code:`default_main_program` is the default program in a lot of APIs.
    For example, the :code:`Executor.run()` will execute the
    :code:`default_main_program` when the program is not specified.
3727

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    Returns:
        Program: main program
3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757

    Examples:
        ..  code-block:: python

            import paddle.fluid as fluid
            
            # Sample Network:
            data = fluid.layers.data(name='image', shape=[3, 224, 224], dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            
            conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None)
            bn1 = fluid.layers.batch_norm(conv1, act='relu')
            pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2)
            conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None)
            bn2 = fluid.layers.batch_norm(conv2, act='relu')
            pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2)
            
            fc1 = fluid.layers.fc(pool2, size=50, act='relu')
            fc2 = fluid.layers.fc(fc1, size=102, act='softmax')
            
            loss = fluid.layers.cross_entropy(input=fc2, label=label)
            loss = fluid.layers.mean(loss)
            opt = fluid.optimizer.Momentum(
                learning_rate=0.1,
                momentum=0.9,
                regularization=fluid.regularizer.L2Decay(1e-4))
            opt.minimize(loss)
            
3758 3759
            print(fluid.default_main_program().num_blocks)
            print(fluid.default_main_program().blocks[0].var('image'))
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    """
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    return _main_program_
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def switch_main_program(program):
    """
    Switch the main program to a new program.
3767

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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):
    """
3782
    Switch the startup program to a new program
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    Args:
        program(Program): The new startup program

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


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@signature_safe_contextmanager
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def program_guard(main_program, startup_program=None):
    """
3798 3799
    Change the global main program and startup program with `"with"` statement.
    Layer functions in the Python `"with"` block will append operators and
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    variables to the new main programs.
3801

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    Examples:
3803 3804 3805
       .. code-block:: python
       
         import paddle.fluid as fluid
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3807 3808 3809 3810 3811
         main_program = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(main_program, startup_program):
             data = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
             hidden = fluid.layers.fc(input=data, size=10, act='relu')
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    Notes: The temporary :code:`Program` can be used if the user does not need
    to construct either of startup program or main program.
3815

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    Examples:
3817
       .. code-block:: python
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3819 3820 3821 3822 3823 3824
         import paddle.fluid as fluid

         main_program = fluid.Program()
         # does not care about startup program. Just pass a temporary value.
         with fluid.program_guard(main_program, fluid.Program()):
             data = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
3825

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3826
    Args:
3827 3828 3829
        main_program(Program): New main program inside `"with"` statement.
        startup_program(Program): New startup program inside `"with"` statement.
            None means not changing startup program.
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    """
    if not isinstance(main_program, Program):
        raise TypeError("main_program should be Program")
    main_program = switch_main_program(main_program)
    if startup_program is not None:
        if not isinstance(startup_program, Program):
            raise TypeError("startup_program should be Program")
        startup_program = switch_startup_program(startup_program)
    yield
    switch_main_program(main_program)
    if startup_program is not None:
        switch_startup_program(startup_program)
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def _get_var(name, program=None):
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    """
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    Get a variable by name from the global block of a program.
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    Args:
        name(str): name of the variable
        program(Program|None): program object.
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        If None, default_global_program() will be used.
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    Returns:
        Variable
    """
    if program is None:
        program = default_main_program()
    assert isinstance(name, str)
3859
    assert isinstance(program, Program)
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    return program.global_block().var(name)
3862 3863


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3864
@signature_safe_contextmanager
L
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def _dygraph_guard(tracer):
    global _dygraph_tracer_
    tmp_trace = _dygraph_tracer_
    _dygraph_tracer_ = tracer
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3870
    yield
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    _dygraph_tracer_ = tmp_trace
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3875
@signature_safe_contextmanager
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def _dygraph_place_guard(place):
    global _dygraph_current_expected_place_
    tmp_place = _dygraph_current_expected_place_
    _dygraph_current_expected_place_ = place
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3881
    yield
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    _dygraph_current_expected_place_ = tmp_place