framework.py 252.7 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.abc import Iterable
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import contextlib
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from .wrapped_decorator import signature_safe_contextmanager, wrap_decorator
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import os
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import re
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import traceback
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import six
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import copy
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from types import MethodType, FunctionType
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import numpy as np
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import subprocess
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import multiprocessing
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import sys
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import logging
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from .. import compat as cpt
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from .proto import framework_pb2
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from . import core
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from . import unique_name
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import paddle.version as fluid_version
import warnings
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import functools
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from .variable_index import _getitem_impl_, _setitem_impl_
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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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    'ipu_shard_guard',
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    'set_ipu_shard',
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    'cuda_places',
    'cpu_places',
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    'xpu_places',
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    'mlu_places',
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    'cuda_pinned_places',
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    '_non_static_mode',
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    'in_dygraph_mode',
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    'is_compiled_with_cinn',
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    'is_compiled_with_cuda',
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    'is_compiled_with_rocm',
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    'is_compiled_with_xpu',
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    'is_compiled_with_npu',
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    'Variable',
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    'require_version',
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    'device_guard',
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    'set_flags',
    'get_flags',
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]
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EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
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CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()

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_dygraph_tracer_ = None
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_in_eager_mode_ = (os.environ.get('FLAGS_enable_eager_mode') == '1')
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_global_expected_place_ = None
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_current_device = None
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global_prog_seed = 0
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_current_pipeline_stage = None
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_already_patch_eager_tensor = False
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_already_patch_varbase = False
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_current_cuda_graph_mode = None
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_global_flags_ = core.globals()
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# Some explanation of our execution system 2022.03
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# For now we have 3 kinds of execution system, since we refactored dygraph mode to
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# build a fast execution system for dynamic mode. But we can't just remove all legacy
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# code once we present the new system for some historical reason. That's why we have
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# these flags.
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#
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# 1. _non_static_mode():
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# _non_static_mode means  we are now running in legacy dygraph mode or dygraph mode.
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# 2. dygraph_mode():
# This flags inidicates we are now running in dygraph mode which called eager mode before.
# 3. _in_legacy_dygraph():
# This flags inidicates we are now running in legacy dygraph mode
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#
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# They have a relation ship as below:
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# Both dygraph_mode and _in_legacy_dygraph are _non_static_mode, but if you are running in
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# dygraph mode means you are not in _in_legacy_dygraph.
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#
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# Why we have to make different of _in_legacy_dygraph and dygraph_mode?
# In some performance issue, we find that python if statement cause server performance problem
# and we need our new dygraph mode becomes as fast as it could be. That's why we make these flags
# to make sure in most case, we find new dygraph mode first with only one if statement.


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def _update_monkey_methods(is_eager):
    """
    Update monkey methods of VarBase or eager.Tensor while
    switching eager mode and legacy mode.
    """
    from paddle import _C_ops
    from .dygraph.varbase_patch_methods import monkey_patch_varbase
    from .dygraph import monkey_patch_math_varbase

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    global _already_patch_eager_tensor
    global _already_patch_varbase

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    assert isinstance(is_eager, bool)
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    # switch into eager mode
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    if is_eager:
        _C_ops.switch_to_eager_ops()
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        if not _already_patch_eager_tensor:
            monkey_patch_varbase()
            monkey_patch_math_varbase()

            _already_patch_eager_tensor = True
    # switch back into legacy mode
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    else:
        _C_ops.switch_to_core_ops()
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        if not _already_patch_varbase:
            monkey_patch_varbase()
            monkey_patch_math_varbase()

            _already_patch_varbase = True
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    # switch Paddle.Tensor bind type
    _switch_tensor_bind_type(is_eager)


def _switch_tensor_bind_type(is_eager):
    import paddle
    if is_eager:
        paddle.Tensor = core.eager.Tensor
    else:
        paddle.Tensor = core.VarBase
    paddle.Tensor.__qualname__ = 'Tensor'
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def _enable_legacy_dygraph():
    global _in_eager_mode_
    _in_eager_mode_ = False
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    _update_monkey_methods(is_eager=False)
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def _disable_legacy_dygraph():
    global _in_eager_mode_
    _in_eager_mode_ = True
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    _update_monkey_methods(is_eager=True)
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def _in_eager_without_dygraph_check():
    global _in_eager_mode_
    return _in_eager_mode_


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# FIXME(dev): We haven't fully verified eager mode on XPU/NPU et.al but
# only GPU/CPU. Remove this after we improve this feature.
_is_first_import_ = True


def _fallback_legacy_dygraph():
    global _in_eager_mode_
    global _is_first_import_
    need_fallback = False
    # Only enable eager on CPU/GPU
    is_not_support = core.is_compiled_with_xpu() or core.is_compiled_with_npu(
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    ) or core.is_compiled_with_ipu() or core.is_compiled_with_mlu()
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    if _in_eager_mode_ and is_not_support:
        # switch into legacy dygraph mode
        warnings.warn(
            "We will fallback into legacy dygraph on NPU/XPU/MLU/IPU/ROCM devices. Because we only support new eager dygraph mode on CPU/GPU currently. "
        )
        _in_eager_mode_ = False
        if not _is_first_import_:
            _enable_legacy_dygraph()
        need_fallback = True

    need_fallback = False
    _is_first_import_ = False

    return need_fallback


# switch into legacy mode if need while import paddle
_fallback_legacy_dygraph()


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def in_dygraph_mode():
    """

    .. note::
        Dynamic graph mode is turn ON by default since paddle 2.0.0

    This API checks whether paddle runs in dynamic graph mode.

    You can turn ON static graph mode by `enable_static <../dygraph/base/disable_dygraph_en.html>`_ ,
    and turn OFF static graph mode by `disable_static <../dygraph/base/enable_dygraph_en.html>`_  .

    Returns:
        bool: Whether paddle runs in dynamic graph mode.

    Examples:
        .. code-block:: python

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

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

            paddle.disable_static()
            print(paddle.in_dynamic_mode())  # True, Now we are in dynamic mode

    """
    return (_dygraph_tracer_ is not None) and _in_eager_mode_


def _in_legacy_dygraph():
    return (not _in_eager_mode_) and (_dygraph_tracer_ is not None)


def _non_static_mode():
    return _dygraph_tracer_ is not None
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@signature_safe_contextmanager
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def _test_eager_guard(place=None):
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    # FIXME(dev): We haven't fully verified eager mode on XPU/NPU et.al but
    # only GPU/CPU. Remove this after we improve this feature.
    already_fallback = _fallback_legacy_dygraph()
    if not already_fallback:
        _disable_legacy_dygraph()
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    try:
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        yield
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    finally:
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        if not already_fallback:
            _enable_legacy_dygraph()
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global_ipu_index = -1
global_ipu_stage = -1
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ipu_index_attr_name = 'ipu_index'
ipu_stage_attr_name = 'ipu_stage'


@signature_safe_contextmanager
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def ipu_shard_guard(index=-1, stage=-1):
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    """
    Used to shard the graph on IPUs. Set each Op run on which IPU in the sharding and which stage in the pipelining.

    Args:
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        index(int, optional): Specify which ipu the Tensor is computed on, (such as '0, 1, 2, 3').
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            The default value is -1, which means the Op only run on IPU 0.
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        stage(int, optional): Specify the computation order of the sharded model(such as '0, 1, 2, 3').
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            The sharded model will be computed from small to large. The default value is -1, 
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            which means no pipelining computation order and run Ops in terms of graph.
    
    **Note**:
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    Only if the enable_manual_shard=True, the 'index' is able to be set not -1. Please refer 
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    to :code:`paddle.static.IpuStrategy` . 
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    Only if the enable_pipelining=True, the 'stage' is able to be set not -1. Please refer 
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    to :code:`paddle.static.IpuStrategy` .
    A index is allowed to match none stage or a stage. A stage is only allowed to match a new or 
    duplicated index.

    Examples:
        .. code-block:: python

            # required: ipu

            import paddle
            paddle.enable_static()
            a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
            with paddle.static.ipu_shard_guard(index=0, stage=0):
                b = a + 1
            with paddle.static.ipu_shard_guard(index=1, stage=1):
                c = b + 1
            with paddle.static.ipu_shard_guard(index=0, stage=2):
                d = c + 1
    """
    if not core.is_compiled_with_ipu():
        raise ValueError(
            "Can not use this function since PaddlePaddle is not compiled with IPU"
        )

    global global_ipu_index
    global global_ipu_stage
    prev_ipu_index = global_ipu_index
    prev_ipu_stage = global_ipu_stage
    global_ipu_index = index
    global_ipu_stage = stage
    try:
        yield
    finally:
        global_ipu_index = prev_ipu_index
        global_ipu_stage = prev_ipu_stage


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def set_ipu_shard(call_func, index=-1, stage=-1):
    """
    Shard the ipu with the given call function. Set every ops in call function to the given ipu sharding.

    Args:
        call_func(Layer|function): Specify the call function to be wrapped.
        index(int, optional): Specify which ipu the Tensor is computed on, (such as ‘0, 1, 2, 3’).
            The default value is -1, which means the Op only run on IPU 0.
        stage(int, optional): Specify the computation order of the sharded model(such as ‘0, 1, 2, 3’).
            The sharded model will be computed from small to large. The default value is -1, 
            which means no pipelining computation order and run Ops in terms of graph.

    Returns:
        The wrapped call function.


    Examples:
        .. code-block:: python

            # required: ipu

            import paddle
            paddle.enable_static()
            a = paddle.static.data(name='data', shape=[None, 1], dtype='float32')
            relu = paddle.nn.ReLU()
            relu = paddle.static.set_ipu_shard(relu, index=1, stage=1)
            relu(a)
    """

    def decorate(func):

        def wrapper(*args, **kwargs):
            with ipu_shard_guard(index=index, stage=stage):
                return func(*args, **kwargs)

        return wrapper

    from .dygraph.layers import Layer
    if not isinstance(call_func, Layer):
        if callable(call_func):
            return decorate(call_func)
        else:
            raise TypeError(
                "Unsupported type. Only accept paddle.nn.Layer or function.")

    # patch paddle.nn.Layer
    class BlockFn(type(call_func)):

        def __call__(self, *args, **kwargs):
            with ipu_shard_guard(index=index, stage=stage):
                return super().__call__(*args, **kwargs)

    BlockFn.__name__ = type(call_func).__name__
    call_func.__class__ = BlockFn
    return call_func


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

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

        Returns:
            None.

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

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid

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

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

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

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

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

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

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

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

    min_version_split = min_version.split('.')
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    min_version_to_check = min_version_split + zero_version[
        len(min_version_split):]
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    if max_version is not None:
        max_version_split = max_version.split('.')
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        max_version_to_check = max_version_split + zero_version[
            len(max_version_split):]
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        if version_cmp(version_installed,
                       max_version_to_check) > 0 or version_cmp(
                           version_installed, min_version_to_check) < 0:
            raise Exception(
                "VersionError: PaddlePaddle version in [%s, %s] required, but %s installed."
                % (min_version, max_version, fluid_version.full_version))
    else:
        if version_cmp(version_installed, min_version_to_check) < 0:
            raise Exception(
                "VersionError: PaddlePaddle version %s or higher is required, but %s installed, "
                "please upgrade your PaddlePaddle to %s or other higher version."
                % (min_version, fluid_version.full_version, min_version))


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def _dygraph_not_support_(func):
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    def __impl__(*args, **kwargs):
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        assert not _non_static_mode(
        ), "We don't support %s in dynamic graph mode" % func.__name__
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        return func(*args, **kwargs)

    return __impl__


def _dygraph_only_(func):
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    def __impl__(*args, **kwargs):
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        assert _non_static_mode(
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        ), "We only support '%s()' in dynamic graph mode, please call 'paddle.disable_static()' to enter dynamic graph mode." % func.__name__
        return func(*args, **kwargs)

    return __impl__


def _static_only_(func):
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    def __impl__(*args, **kwargs):
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        assert not _non_static_mode(
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        ), "In PaddlePaddle 2.x, we turn on dynamic graph mode by default, and '%s()' is only supported in static graph mode. So if you want to use this api, please call 'paddle.enable_static()' before this api to enter static graph mode." % func.__name__
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        return func(*args, **kwargs)

    return __impl__


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def _set_pipeline_stage(stage):
    global _current_pipeline_stage
    _current_pipeline_stage = stage


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# NOTE(zhiqiu): This decorator is used for the APIs of Variable which is only
# used to make Variable and VarBase has same interfaces, like numpy. Since VarBase is not exposed in our
# official docments, logically, we want to keep VarBase and logically consistent. While, actually,
# in our implementation, there some APIs not supported, like numpy, because Variable contains the desc.
# So, those APIs are listed under class Variable to generate docs only.
# TODO(zhiqiu): We should make VarBase consistent with Variable in future, for example, by inheritting
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# same base class.
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def _fake_interface_only_(func):
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    def __impl__(*args, **kwargs):
        raise AssertionError(
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            "'%s' only can be called by `paddle.Tensor` in dynamic graph mode. Suggestions:\n"
            "  1. If you are in static graph mode, you can switch to dynamic graph mode by turning off `paddle.enable_static()` or calling `paddle.disable_static()`.\n"
            "  2. If you are using `@paddle.jit.to_static`, you can turn off ProgramTranslator by calling `paddle.jit.ProgramTranslator().enable(False)`. "
            "If you have to translate dynamic graph to static graph, please use other API to replace '%s'."
            % (func.__name__, func.__name__))
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    return __impl__


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

    return wrapper


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dygraph_not_support = wrap_decorator(_dygraph_not_support_)
dygraph_only = wrap_decorator(_dygraph_only_)
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static_only = wrap_decorator(_static_only_)
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fake_interface_only = wrap_decorator(_fake_interface_only_)
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def _dygraph_tracer():
    return _dygraph_tracer_
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def _global_flags():
    return _global_flags_


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def _current_expected_place():
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    global _global_expected_place_
    if _global_expected_place_ is None:
        if core.is_compiled_with_cuda():
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            try:
                device_count = core.get_cuda_device_count()
            except Exception as e:
                device_count = 0
            if device_count > 0:
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                _global_expected_place_ = core.CUDAPlace(_cuda_ids()[0])
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            else:
                warnings.warn(
                    "You are using GPU version Paddle, but your CUDA device is not set properly. CPU device will be used by default."
                )
                _global_expected_place_ = core.CPUPlace()
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        elif core.is_compiled_with_xpu():
            try:
                device_count = core.get_xpu_device_count()
            except Exception as e:
                device_count = 0
            if device_count > 0:
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                _global_expected_place_ = core.XPUPlace(_xpu_ids()[0])
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            else:
                warnings.warn(
                    "You are using XPU version Paddle, but your XPU device is not set properly. CPU device will be used by default."
                )
                _global_expected_place_ = core.CPUPlace()
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        elif core.is_compiled_with_mlu():
            try:
                device_count = core.get_mlu_device_count()
            except Exception as e:
                device_count = 0
            if device_count > 0:
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                _global_expected_place_ = core.MLUPlace(_mlu_ids()[0])
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            else:
                warnings.warn(
                    "You are using MLU version Paddle, but your MLU device is not set properly. CPU device will be used by default."
                )
                _global_expected_place_ = core.CPUPlace()
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        else:
            _global_expected_place_ = core.CPUPlace()

    return _global_expected_place_


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


def _set_expected_place(place):
    global _global_expected_place_
    _global_expected_place_ = place
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    _set_dygraph_tracer_expected_place(place)
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# TODO(zhiqiu): remove this function.
def _var_base_to_np(var_base):
    """	
    convert VarBase tp numpy	
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    Args:	
        var_base(VarBase) : the VarBase to convert	
    Returns (np.ndarray): the np.ndarray contain the value of VarBase	
    """

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

    return var_base.numpy()


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


def _cuda_ids():
    gpus_env = os.getenv("FLAGS_selected_gpus")
    if gpus_env:
        device_ids = [int(s) for s in gpus_env.split(",")]
    else:
        device_ids = six.moves.range(core.get_cuda_device_count())
    return device_ids
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def _xpu_ids():
    xpus_env = os.getenv("FLAGS_selected_xpus")
    if xpus_env:
        device_ids = [int(s) for s in xpus_env.split(",")]
    else:
        device_ids = six.moves.range(core.get_xpu_device_count())
    return device_ids


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def _npu_ids():
    npus_env = os.getenv("FLAGS_selected_npus")
    if npus_env:
        device_ids = [int(s) for s in npus_env.split(",")]
    else:
        device_ids = six.moves.range(core.get_npu_device_count())
    return device_ids


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def _mlu_ids():
    mlus_env = os.getenv("FLAGS_selected_mlus")
    if mlus_env:
        device_ids = [int(s) for s in mlus_env.split(",")]
    else:
        device_ids = six.moves.range(core.get_mlu_device_count())
    return device_ids


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

    Returns (bool): support xpu or not.

    Examples:
        .. code-block:: python

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


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

    Returns (bool): support npu or not.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            support_npu = fluid.is_compiled_with_npu()
    """
    return core.is_compiled_with_npu()


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def disable_signal_handler():
    """
    Reset signal handler registered by Paddle.

    Paddle installs signal handlers at C++ level to log debug information upon failing.
    However, conflicts can happen if another python module is making use of such signal.
    Such being the case, one may disblae paddle signal handler via this interface.
    
    Known frameworks that require disabling signal handler includes:
    1. TVM
    2. ADLIK

    Make sure you called paddle.disable_signal_handler() before using above mentioned frameworks.

    Returns: None 

    Examples:
        .. code-block:: python

            import paddle
            paddle.disable_signal_handler()
    """
    core.disable_signal_handler()


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

    Returns (bool): `True` if CINN is currently available, otherwise `False`.

    Examples:
        .. code-block:: python

            import paddle
            support_cinn = paddle.device.is_compiled_with_cinn()
    """
    return core.is_compiled_with_cinn()


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

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

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            import paddle
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            support_gpu = paddle.device.is_compiled_with_cuda()
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    """
    return core.is_compiled_with_cuda()


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def is_compiled_with_rocm():
    """
    Whether this whl package can be used to run the model on AMD or Hygon GPU(ROCm).

    Returns (bool): `True` if ROCm is currently available, otherwise `False`.

    Examples:
        .. code-block:: python

            import paddle
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            support_gpu = paddle.device.is_compiled_with_rocm()
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    """
    return core.is_compiled_with_rocm()


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

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

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            import paddle
            import paddle.static as static
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            # required: gpu
            
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            paddle.enable_static()

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


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def xpu_places(device_ids=None):
    """
    **Note**:
        For multi-card tasks, please use `FLAGS_selected_xpus` environment variable to set the visible XPU device.
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        This function creates a list of :code:`paddle.XPUPlace` objects.
        If :code:`device_ids` is None, environment variable of
        :code:`FLAGS_selected_xpus` would be checked first. For example, if
        :code:`FLAGS_selected_xpus=0,1,2`, the returned list would
        be [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
        If :code:`FLAGS_selected_xpus` is not set, all visible
        xpu places would be returned.
        If :code:`device_ids` is not None, it should be the device
        ids of XPUs. For example, if :code:`device_ids=[0,1,2]`,
        the returned list would be 
        [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
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    Parameters:
        device_ids (list or tuple of int, optional): list of XPU device ids.
    Returns:
        list of paddle.XPUPlace: Created XPU place list.
    Examples:
        .. code-block:: python
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            # required: xpu

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            import paddle
            import paddle.static as static
            
            paddle.enable_static()
            xpu_places = static.xpu_places()
    """
    assert core.is_compiled_with_xpu(), \
        "Not compiled with XPU"
    if device_ids is None:
        device_ids = _xpu_ids()
    elif not isinstance(device_ids, (list, tuple)):
        device_ids = [device_ids]
    return [core.XPUPlace(dev_id) for dev_id in device_ids]


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def npu_places(device_ids=None):
    """
    **Note**:
        For multi-card tasks, please use `FLAGS_selected_npus` environment variable to set the visible NPU device.
    
    This function creates a list of :code:`paddle.NPUPlace` objects.
    If :code:`device_ids` is None, environment variable of
    :code:`FLAGS_selected_npus` would be checked first. For example, if
    :code:`FLAGS_selected_npus=0,1,2`, the returned list would
    be [paddle.NPUPlace(0), paddle.NPUPlace(1), paddle.NPUPlace(2)].
    If :code:`FLAGS_selected_npus` is not set, all visible
    npu places would be returned.
    If :code:`device_ids` is not None, it should be the device
    ids of NPUs. For example, if :code:`device_ids=[0,1,2]`,
    the returned list would be 
    [paddle.NPUPlace(0), paddle.NPUPlace(1), paddle.NPUPlace(2)].
    
    Parameters:
        device_ids (list or tuple of int, optional): list of NPU device ids.
    Returns:
        list of paddle.NPUPlace: Created NPU place list.
    Examples:
        .. code-block:: python

            # required: npu

            import paddle
            import paddle.static as static
            
            paddle.enable_static()
            npu_places = static.npu_places()
    """
    assert core.is_compiled_with_npu(), \
        "Not compiled with NPU"
    if device_ids is None:
        device_ids = _npu_ids()
    elif not isinstance(device_ids, (list, tuple)):
        device_ids = [device_ids]
    return [core.NPUPlace(dev_id) for dev_id in device_ids]


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

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

            cpu_places = static.cpu_places()
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    """

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


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

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

    """
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    assert core.is_compiled_with_cuda(), \
        "Not compiled with CUDA"
    if device_count is None:
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        device_count = len(_cuda_ids())
    return [core.CUDAPinnedPlace()] * device_count
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def mlu_places(device_ids=None):
    """
    **Note**:
        For multi-card tasks, please use `FLAGS_selected_mlus` environment variable to set the visible MLU device.
        This function creates a list of :code:`paddle.device.MLUPlace` objects.
        If :code:`device_ids` is None, environment variable of
        :code:`FLAGS_selected_mlus` would be checked first. For example, if
        :code:`FLAGS_selected_mlus=0,1,2`, the returned list would
        be [paddle.device.MLUPlace(0), paddle.device.MLUPlace(1), paddle.device.MLUPlace(2)].
        If :code:`FLAGS_selected_mlus` is not set, all visible
        mlu places would be returned.
        If :code:`device_ids` is not None, it should be the device
        ids of MLUs. For example, if :code:`device_ids=[0,1,2]`,
        the returned list would be
        [paddle.device.MLUPlace(0), paddle.device.MLUPlace(1), paddle.device.MLUPlace(2)].

    Parameters:
        device_ids (list or tuple of int, optional): list of MLU device ids.

    Returns:
        list of paddle.device.MLUPlace: Created MLU place list.

    Examples:
        .. code-block:: python

            # required: mlu

            import paddle
            import paddle.static as static

            paddle.enable_static()
            mlu_places = static.mlu_places()
    """
    assert core.is_compiled_with_mlu(), \
        "Not compiled with MLU"
    if device_ids is None:
        device_ids = _mlu_ids()
    elif not isinstance(device_ids, (list, tuple)):
        device_ids = [device_ids]
    return [core.MLUPlace(dev_id) for dev_id in device_ids]


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

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

    def parent(self):
        return self._parent

    def name(self):
        return self._name


_name_scope = NameScope()


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@signature_safe_contextmanager
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def name_scope(prefix=None):
    """
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    Generate hierarchical name prefix for the operators in Static Graph.
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    Note: 
        This should only used for debugging and visualization purpose.
        Don't use it for serious analysis such as graph/program transformations.
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        Don't use it in dygraph, since it will cause memory leak.
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    Args:
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        prefix(str, optional): prefix. Default is none.
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    Examples:
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        .. code-block:: python
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          import paddle
          paddle.enable_static()
          with paddle.static.name_scope("s1"):
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             a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
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             b = a + 1
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             with paddle.static.name_scope("s2"):
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                c = b * 1
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             with paddle.static.name_scope("s3"):
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                d = c / 1
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          with paddle.static.name_scope("s1"):
                f = paddle.tensor.pow(d, 2.0)
          with paddle.static.name_scope("s4"):
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                g = f - 1

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


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

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def convert_np_dtype_to_dtype_(np_dtype):
1147 1148
    """
    Convert the data type in numpy to the data type in Paddle
1149

1150
    Args:
1151
        np_dtype(np.dtype): the data type in numpy.
1152

1153 1154
    Returns:
        core.VarDesc.VarType: the data type in Paddle.
1155 1156

    """
1157 1158
    dtype = np.dtype(np_dtype)
    if dtype == np.float32:
1159
        return core.VarDesc.VarType.FP32
1160
    elif dtype == np.float64:
1161
        return core.VarDesc.VarType.FP64
1162
    elif dtype == np.float16:
1163
        return core.VarDesc.VarType.FP16
1164
    elif dtype == np.int32:
1165
        return core.VarDesc.VarType.INT32
1166
    elif dtype == np.int16:
1167
        return core.VarDesc.VarType.INT16
1168
    elif dtype == np.int64:
1169
        return core.VarDesc.VarType.INT64
1170
    elif dtype == np.bool_:
1171
        return core.VarDesc.VarType.BOOL
1172
    elif dtype == np.uint16:
1173 1174 1175
        # since there is still no support for bfloat16 in NumPy,
        # uint16 is used for casting bfloat16
        return core.VarDesc.VarType.BF16
1176 1177
    elif dtype == np.uint8:
        return core.VarDesc.VarType.UINT8
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    elif dtype == np.int8:
        return core.VarDesc.VarType.INT8
1180 1181 1182 1183
    elif dtype == np.complex64:
        return core.VarDesc.VarType.COMPLEX64
    elif dtype == np.complex128:
        return core.VarDesc.VarType.COMPLEX128
1184
    else:
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        raise ValueError("Not supported numpy dtype %s" % dtype)
1186 1187 1188


def dtype_is_floating(dtype):
1189 1190 1191
    """
    Check the data type is floating or not.
    Args:
1192
        dtype(np.dtype|core.VarDesc.VarType): data type.
1193 1194 1195 1196 1197
            Could be numpy format or Paddle format

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

    """
1198
    if not isinstance(dtype, core.VarDesc.VarType):
1199 1200
        dtype = convert_np_dtype_to_dtype_(dtype)

1201 1202 1203 1204
    return dtype in [
        core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
        core.VarDesc.VarType.FP64
    ]
1205 1206


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def _debug_string_(proto, throw_on_error=True):
1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218
    """
    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:
1221 1222 1223
        raise ValueError(
            "{0} are not initialized.\nThe message is {1}:\n".format(
                error_fields, proto))
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    return proto.__str__()


1227 1228 1229 1230 1231 1232 1233 1234 1235 1236
def _varbase_creator(type=core.VarDesc.VarType.LOD_TENSOR,
                     name=None,
                     shape=None,
                     dtype=None,
                     persistable=None,
                     **kwargs):
    if dtype is not None:
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)

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    if _in_eager_mode_:
1238
        eager_tensor = core.eager.Tensor(
1239
            dtype if dtype else core.VarDesc.VarType.FP32,
1240 1241 1242
            list(shape) if shape else [], name,
            type if type else core.VarDesc.VarType.LOD_TENSOR,
            True if persistable else False)
1243 1244
        eager_tensor.retain_grads()
        return eager_tensor
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    else:
        return core.VarBase(dtype if dtype else core.VarDesc.VarType.FP32,
1247 1248 1249
                            list(shape) if shape else [], name,
                            type if type else core.VarDesc.VarType.LOD_TENSOR,
                            True if persistable else False)
1250 1251 1252


class VariableMetaClass(type):
1253

1254 1255 1256 1257
    @classmethod
    def __instancecheck__(cls, instance):
        t = type(instance)
        if in_dygraph_mode():
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            return issubclass(t, core.eager.Tensor)
1259
        else:
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            if _in_legacy_dygraph():
                return issubclass(t, core.VarBase)
1262 1263 1264 1265
            return issubclass(t, Variable)


class ParameterMetaClass(VariableMetaClass):
1266

1267 1268 1269 1270
    @classmethod
    def __instancecheck__(cls, instance):
        t = type(instance)
        if in_dygraph_mode():
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            return issubclass(t, EagerParamBase)
1272
        else:
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            if _in_legacy_dygraph():
                return issubclass(t, ParamBase)
1275 1276 1277 1278
            return issubclass(t, Parameter)


@six.add_metaclass(VariableMetaClass)
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class Variable(object):
1280
    """
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    **Notes**:
1282
        **The constructor of Variable should not be invoked directly.**
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1284 1285
        **In Static Graph Mode: Please use** `Block.create_var` **to create a Static variable which has no data until being feed.**

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

    In Fluid, every input and output of an OP is a variable. In most
1289
    cases, variables are used for holding different kinds of data or training
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    labels. A variable belongs to a :ref:`api_guide_Block_en` . All variable has its own name and
    two variables in different :ref:`api_guide_Block_en` could have the same name.
1292

1293
    There are many kinds of variables. Each kind of them has its own attributes
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    and usages. Please refer to the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details.
1295

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    Most of a Variable's member variables can be set to be None. It mean
1297
    it is not available or will be specified later.
1298

1299
    Examples:
1300 1301
        In Static Graph Mode:

1302 1303
        .. code-block:: python

1304
            import paddle.fluid as fluid
1305
            cur_program = fluid.Program()
1306 1307 1308 1309
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
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        In `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_  Mode:
1312 1313 1314 1315 1316 1317 1318 1319 1320

        .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

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

1321 1322
    """

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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,
1330
                 capacity=None,
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                 persistable=None,
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                 error_clip=None,
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                 stop_gradient=False,
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                 is_data=False,
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                 need_check_feed=False,
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                 belong_to_optimizer=False,
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                 **kwargs):
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        self.block = block
        if name is None:
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            name = unique_name.generate('_generated_var')
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        if dtype is not None:
1343
            if not isinstance(dtype, core.VarDesc.VarType):
1344
                dtype = convert_np_dtype_to_dtype_(dtype)
1345

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        if dtype == core.VarDesc.VarType.STRINGS:
            type = core.VarDesc.VarType.STRINGS
            lod_level = None

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        self.belong_to_optimizer = belong_to_optimizer

1352 1353 1354 1355 1356
        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))
1357

1358 1359 1360
        if self.desc is None:
            self.desc = self.block.desc.var(cpt.to_bytes(name))
            is_new_var = True
1361

1362 1363 1364
        if is_new_var:
            self.desc.set_type(type)
        elif self.desc.type() != type:
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            raise ValueError("Variable '{0}' has been created before. The "
                             "previous type is {1}, the new type is {2}. They"
1367 1368
                             " are not matched".format(self.name,
                                                       self.desc.type(), type))
1369

1370
        if shape is not None:
1371
            if is_new_var:
1372 1373 1374 1375 1376 1377
                self.desc.set_shape(shape)
            else:
                old_shape = self.shape
                shape = tuple(shape)
                if shape != old_shape:
                    raise ValueError(
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                        "Variable '{0}' has been created before. The previous "
                        "shape is {1}, the new shape is {2}. They are not "
1380 1381 1382 1383 1384 1385 1386
                        "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:
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                    raise ValueError("Variable '{0}' has been created before. "
                                     "The previous data type is {1}, the new "
1389 1390 1391 1392 1393 1394 1395 1396 1397
                                     "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:
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                    raise ValueError("Variable '{0}' has been created before. "
                                     "The previous lod_level is {1}, the new "
1400 1401 1402 1403 1404 1405 1406 1407 1408
                                     "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(
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                        "Variable '{0}' has been created before."
                        "The previous persistable is {1}, the new "
1411 1412
                        "persistable is {2}. They are not matched".format(
                            self.name, self.persistable, persistable))
1413

1414 1415
        if need_check_feed and is_new_var:
            self.desc.set_need_check_feed(need_check_feed)
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1416

1417 1418 1419 1420 1421 1422 1423
        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
1424

1425 1426
        self.block.vars[name] = self
        self.op = None
1427
        self.stop_gradient = stop_gradient
1428
        self.is_data = is_data
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1429

1430 1431 1432
    def detach(self):
        """
        Returns a new Variable, detached from the current graph.
1433 1434
        It will share data with origin Variable and without tensor copy.
        In addition, the detached Variable doesn't provide gradient propagation.
1435

1436
        Returns:
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             ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
1438 1439 1440 1441

        Examples:
            .. code-block:: python

1442
                import paddle
1443

1444 1445 1446 1447
                paddle.enable_static()

                # create a static Variable
                x = paddle.static.data(name='x', shape=[3, 2, 1])
1448

1449 1450
                # create a detached Variable
                y = x.detach()
1451
        """
1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463

        assert self.type == core.VarDesc.VarType.SELECTED_ROWS or \
            self.type == core.VarDesc.VarType.LOD_TENSOR, \
            "only support a variable with SELECTED_ROWS or LOD_TENSOR to be detached"

        output = self.block.create_var(
            name=unique_name.generate_with_ignorable_key("detach_" + self.name),
            dtype=self.dtype,
            type=self.type,
            persistable=self.persistable,
            stop_gradient=True)

1464 1465 1466
        self.block.append_op(type='share_data',
                             inputs={'X': [self]},
                             outputs={'Out': [output]})
1467
        return output
1468

1469
    @fake_interface_only
1470
    def numpy(self):
1471
        """
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1472
        **Notes**:
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1473
            **This API is ONLY available in Dygraph mode**
1474

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        Returns a numpy array shows the value of current :ref:`api_guide_Variable_en`
1476 1477 1478 1479 1480

        Returns:
            ndarray: The numpy value of current Variable.

        Returns type:
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            ndarray: dtype is same as current Variable
1482 1483 1484 1485 1486 1487

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
1488
                from paddle.fluid.dygraph import Linear
1489 1490 1491 1492
                import numpy as np

                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                with fluid.dygraph.guard():
1493
                    linear = Linear(32, 64)
1494
                    data = to_variable(data)
1495
                    x = linear(data)
1496 1497 1498
                    print(x.numpy())

        """
1499
        pass
1500

1501
    @fake_interface_only
1502
    def backward(self, retain_graph=False):
1503
        """
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1504
        **Notes**:
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1505
            **This API is ONLY available in Dygraph mode**
1506

1507
        Run backward of current Graph which starts from current Tensor.
1508

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1509
        Args:
1510 1511 1512 1513
            retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would
                like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter
                :code:`retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient.
                Defaults to False.
1514

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1515 1516
        Returns:
            NoneType: None
1517 1518 1519 1520 1521

        Examples:
            .. code-block:: python

                import numpy as np
1522 1523
                import paddle
                paddle.disable_static()
1524 1525

                x = np.ones([2, 2], np.float32)
1526 1527 1528 1529 1530 1531 1532
                inputs = []
                for _ in range(10):
                    tmp = paddle.to_tensor(x)
                    # if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since
                    # there is no one need gradient on it.
                    tmp.stop_gradient=False
                    inputs.append(tmp)
1533 1534
                ret = paddle.add_n(inputs)
                loss = paddle.sum(ret)
1535
                loss.backward()
1536 1537

        """
1538
        pass
1539

1540
    @fake_interface_only
1541
    def gradient(self):
1542
        """
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1543
        **Notes**:
T
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1544
            **This API is ONLY available in Dygraph mode**
1545 1546 1547

        Get the Gradient of Current Variable

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1548
        Returns:
1549
            ndarray or tuple of ndarray: if Variable's type is LoDTensor, return numpy value of the gradient of current Variable, if Variable's type is SelectedRows, return tuple of ndarray, first element of tuple is numpy value of the gradient of current Variable, second element of tuple is numpy value of the rows of current Variable.
1550 1551 1552 1553 1554 1555 1556

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

1557
                # example1: return ndarray
1558 1559 1560 1561 1562 1563 1564 1565 1566
                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
1567
                    loss2.backward()
1568 1569
                    print(loss2.gradient())

1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582
                # example2: return tuple of ndarray
                with fluid.dygraph.guard():
                    embedding = fluid.dygraph.Embedding(
                        size=[20, 32],
                        param_attr='emb.w',
                        is_sparse=True)
                    x_data = np.arange(12).reshape(4, 3).astype('int64')
                    x_data = x_data.reshape((-1, 3, 1))
                    x = fluid.dygraph.base.to_variable(x_data)
                    out = embedding(x)
                    out.backward()
                    print(embedding.weight.gradient())

1583
        """
1584
        pass
1585

1586
    @fake_interface_only
1587
    def clear_gradient(self):
1588
        """
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        **Notes**:
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1590
            **1. This API is ONLY available in Dygraph mode**
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1591 1592

            **2. Use it only Variable has gradient, normally we use this for Parameters since other temporal Variable will be deleted by Python's GC**
1593

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        Clear  (set to ``0`` ) the Gradient of Current Variable
1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612

        Returns:  None

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
1613
                    loss2.backward()
1614 1615 1616 1617 1618
                    print(loss2.gradient())
                    loss2.clear_gradient()
                    print("After clear {}".format(loss2.gradient()))

        """
1619
        pass
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1621 1622 1623 1624
    @fake_interface_only
    def register_hook(self, hook):
        pass

1625
    def __str__(self):
1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641
        return self._to_readable_code()

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

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

        Returns:
            string: The formatted Variable string.

        Examples:
            .. code-block:: python

1642 1643
                import paddle
                import paddle.static as static
1644

1645 1646 1647
                paddle.enable_static()

                cur_program = static.Program()
1648 1649 1650 1651 1652 1653
                cur_block = cur_program.current_block()
                new_variable = cur_block.create_var(name="X",
                                                    shape=[-1, 23, 48],
                                                    dtype='float32')
                print(new_variable._to_readable_code())
        """
1654 1655
        # VarType.LOD_TENSOR -> LOD_TENSOR
        type_str = str(self.type).split('.')[1]
1656
        if self.type == core.VarDesc.VarType.SELECTED_ROWS or self.type == core.VarDesc.VarType.LOD_TENSOR:
1657 1658
            dtype_str = str(self.dtype).split('.')[1]
            var_str = "{name} : {type}.shape{shape}.dtype({dtype}).stop_gradient({stop_gradient})".\
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                format(name=self.name, type=type_str, shape=self.shape,
                       dtype=dtype_str, stop_gradient=self.stop_gradient)
1661
        else:
1662 1663
            var_str = "{name} : {type})".\
                format(name=self.name, type=type_str)
1664

1665
        if self.is_parameter:
1666 1667 1668 1669 1670 1671 1672 1673 1674 1675
            if self.trainable:
                var_str = "trainable param " + var_str
            else:
                var_str = "param " + var_str
        else:
            var_str = "var " + var_str

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

1676
        from paddle.distributed.auto_parallel.dist_context import get_default_distributed_context
1677
        dist_context = get_default_distributed_context()
1678 1679
        dist_tensor = dist_context.get_dist_tensor_for_program(self)
        if dist_tensor is not None:
1680 1681
            var_str += ", {name} = {value}".format(name="dist_attr",
                                                   value=dist_tensor)
1682

1683
        return var_str
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F
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    def to_string(self, throw_on_error, with_details=False):
1686 1687 1688
        """
        Get debug string.

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1689 1690 1691 1692 1693
        Args:

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

            with_details (bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default value is False;
1694

1695 1696
        Returns:
            str: The debug string.
1697 1698 1699 1700 1701

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
1702
                import paddle
1703

1704
                paddle.enable_static()
1705 1706 1707 1708 1709
                cur_program = fluid.Program()
                cur_block = cur_program.current_block()
                new_variable = cur_block.create_var(name="X",
                                                    shape=[-1, 23, 48],
                                                    dtype='float32')
1710
                print(new_variable.to_string(True))
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                print("=============with detail===============")
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                print(new_variable.to_string(True, True))
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        """
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        assert isinstance(throw_on_error, bool) and isinstance(
            with_details, bool)
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        protostr = self.desc.serialize_to_string()
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        proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
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        res_str = _debug_string_(proto, throw_on_error)
        if with_details:
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            additional_attr = ("error_clip", )
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            for attr_name in additional_attr:
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                res_str += "%s: %s\n" % (attr_name,
                                         cpt.to_text(getattr(self, attr_name)))

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        return res_str
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    __repr__ = __str__

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    def element_size(self):
        """
        Returns the size in bytes of an element in the Tensor.
        
        Examples:
          .. code-block:: python

            import paddle
            paddle.enable_static()

            x = paddle.static.data(name='x1', shape=[3, 2], dtype='bool')
            x.element_size() # 1

            x = paddle.static.data(name='x2', shape=[3, 2], dtype='int16')
            x.element_size() # 2

            x = paddle.static.data(name='x3', shape=[3, 2], dtype='float16')
            x.element_size() # 2

            x = paddle.static.data(name='x4', shape=[3, 2], dtype='float32')
            x.element_size() # 4

            x = paddle.static.data(name='x5', shape=[3, 2], dtype='float64')
            x.element_size() # 8
        """
        return self.desc.element_size()

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

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

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            import numpy as np

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

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


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

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

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

        Examples:
          .. code-block:: python

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

        Examples:
          .. code-block:: python

            import paddle
            new_parameter = paddle.static.create_parameter(name="X",
                                                shape=[10, 23, 48],
                                                dtype='float32')
            if new_parameter.is_parameter:
                print("Current var is a Parameter")
            else:
                print("Current var is not a Parameter")

            # Current var is a Parameter
        """
        return self.desc.is_parameter()

    @is_parameter.setter
    def is_parameter(self, p):
        self.desc.set_is_parameter(p)

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    @property
    def name(self):
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        """
        Indicating name of current Variable

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

        Examples:
          .. code-block:: python

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

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

          import paddle.fluid as fluid

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

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

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

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

        Examples:
          .. code-block:: python

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

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

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

        Examples:
          .. code-block:: python

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

        **Notes**:

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

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

        Examples:
          .. code-block:: python

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            import paddle
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            import paddle.fluid as fluid
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            paddle.enable_static()
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            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("LoD Level of current Var is: {}".format(new_variable.lod_level))
        """
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        if self.type == core.VarDesc.VarType.SELECTED_ROWS:
            raise Exception("SelectedRows DO NOT supprt lod")
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        if self.type == core.VarDesc.VarType.STRINGS:
            return None
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        return self.desc.lod_level()
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    @property
    def type(self):
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        """
        Indicating Type of current Variable

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

        Examples:
          .. code-block:: python

            import paddle.fluid as fluid
            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_variable = cur_block.create_var(name="X",
                                                shape=[-1, 23, 48],
                                                dtype='float32')
            print("Type of current Var is: {}".format(new_variable.type))
        """
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        return self.desc.type()
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    @property
    def T(self):
        """
        Permute current Variable with its dimensions reversed.

        If `n` is the dimensions of `x` , `x.T` is equivalent to `x.transpose([n-1, n-2, ..., 0])`.

        Examples:

            .. code-block:: python

                import paddle
                paddle.enable_static()

                x = paddle.ones(shape=[2, 3, 5])
                x_T = x.T

                exe = paddle.static.Executor()
                x_T_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_T])[0]
                print(x_T_np.shape)
                # (5, 3, 2)
        """
        if len(self.shape) == 1:
            return self
        perm = []
        for i in range(len(self.shape)):
            perm.insert(0, i)

        out = self.block.create_var(
            name=unique_name.generate_with_ignorable_key(self.name + '.tmp'),
            dtype=self.dtype,
            type=self.type,
            persistable=False,
            stop_gradient=False)
        input_shape = self.block.create_var(
            name=unique_name.generate_with_ignorable_key(self.name + '.tmp'),
            dtype=self.dtype,
            type=core.VarDesc.VarType.LOD_TENSOR,
            persistable=False,
            stop_gradient=False)

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        self.block.append_op(type='transpose2',
                             inputs={'X': [self]},
                             outputs={
                                 'Out': [out],
                                 'XShape': [input_shape]
                             },
                             attrs={'axis': perm})
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        return out

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    def clone(self):
        """
        Returns a new static Variable, which is the clone of the original static
        Variable. It remains in the current graph, that is, the cloned Variable 
        provides gradient propagation. Calling ``out = tensor.clone()`` is same
        as ``out = assign(tensor)`` .

        Returns:
            Variable: The cloned Variable.

        Examples:
            .. code-block:: python

                import paddle

                paddle.enable_static()

                # create a static Variable
                x = paddle.static.data(name='x', shape=[3, 2, 1])
                # create a cloned Variable
                y = x.clone()

        """
        output = self.block.create_var(
            name=unique_name.generate_with_ignorable_key(self.name + "_clone"),
            dtype=self.dtype,
            type=self.type,
            persistable=self.persistable,
            stop_gradient=self.stop_gradient)

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        self.block.append_op(type='assign',
                             inputs={'X': [self]},
                             outputs={'Out': [output]})
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        return output

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    def _set_error_clip(self, error_clip):
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        """
        Set the error_clip.

        Args:
            error_clip(BaseErrorClipAttr) : The new error_clip.

        Returns:
            None
        """
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        self.error_clip = error_clip

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

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

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

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

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

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

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

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

        # Compute start.
        if slice.start is None:
            start = upper if step < 0 else lower
        else:
            start = slice.start
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            start = max(start +
                        length, lower) if start < 0 else min(start, upper)
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        # 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
                             })
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        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,
                             })
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        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:
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                        vars.append(self._sliceVar([axis], [start],
                                                   [start + 1]))
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                        start += step
                else:
                    while start > stop:
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                        vars.append(self._sliceVar([axis], [start],
                                                   [start + 1]))
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                        start += step
                return self._concatVar(vars, axis)
        elif isinstance(item, int):
            if self.shape[axis] < 0:
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                return self._cloneVar(True)
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            index = int(item)
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            if (index > 0 and index >= self.shape[axis]) \
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                    or (index < 0 and (index + self.shape[axis]) < 0):
                raise IndexError("invalid index")
            return self._sliceVar([axis], [index], [index + 1])
        else:
            raise IndexError("Valid index accept int or slice or tuple")

    def __getitem__(self, item):
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        return _getitem_impl_(self, item)
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    def __setitem__(self, item, value):
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        return _setitem_impl_(self, item, value)
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    def get_value(self, scope=None):
        """
        Get the value of variable in given scope. 

        Args:
            scope(Scope, optional) : If `scope` is None, it will be set to global scope 
                obtained through 'paddle.static.global_scope()'. Otherwise, use `scope`.
                Default: None

        Returns:
            Tensor: the value in given scope.

        Examples:
            .. code-block:: python

                import paddle
                import paddle.static as static 
                import numpy as np

                paddle.enable_static()

                x = static.data(name="x", shape=[10, 10], dtype='float32')

                y = static.nn.fc(x, 10, name='fc')
                place = paddle.CPUPlace()
                exe = static.Executor(place)
                prog = paddle.static.default_main_program()
                exe.run(static.default_startup_program())
                inputs = np.ones((10, 10), dtype='float32')
                exe.run(prog, feed={'x': inputs}, fetch_list=[y, ])
                path = 'temp/tensor_'
                for var in prog.list_vars():
                    if var.persistable:
                        t = var.get_value()
                        paddle.save(t, path+var.name+'.pdtensor')

                for var in prog.list_vars():
                    if var.persistable:
                        t_load = paddle.load(path+var.name+'.pdtensor')
                        var.set_value(t_load)
        """
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        # The 'framework' is a low-level module, and 'executor'
        # can not be imported at the begainning of this file.
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        # Therefore, the above two modules are dynamically imported.
        from .executor import global_scope
        if scope is not None and not isinstance(scope, core._Scope):
            raise TypeError(
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                "`scope` should be None or `paddle.static.Scope` type, but received {}."
                .format(type(scope)))
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        if scope is None:
            scope = global_scope()
        var_temp = scope.find_var(self.name)
        if var_temp is None:
            raise ValueError("Can not find Variable '{}' in the Scope.".format(
                self.name))
        t = var_temp.get_tensor()
        return t

    def set_value(self, value, scope=None):
        '''
        Set the value to the tensor in given scope. 

        Args:
            value(Tensor/ndarray) : The value to be set.
            scope(Scope, optional) : If `scope` is None, it will be set to global scope 
                obtained through 'paddle.static.global_scope()'. Otherwise, use `scope`.
                Default: None

        Returns:
            None
        
        Examples:
            .. code-block:: python

                import paddle
                import paddle.static as static 
                import numpy as np

                paddle.enable_static()

                x = static.data(name="x", shape=[10, 10], dtype='float32')

                y = static.nn.fc(x, 10, name='fc')
                place = paddle.CPUPlace()
                exe = static.Executor(place)
                prog = paddle.static.default_main_program()
                exe.run(static.default_startup_program())
                inputs = np.ones((10, 10), dtype='float32')
                exe.run(prog, feed={'x': inputs}, fetch_list=[y, ])
                path = 'temp/tensor_'
                for var in prog.list_vars():
                    if var.persistable:
                        t = var.get_value()
                        paddle.save(t, path+var.name+'.pdtensor')

                for var in prog.list_vars():
                    if var.persistable:
                        t_load = paddle.load(path+var.name+'.pdtensor')
                        var.set_value(t_load)
        '''

        # The 'framework' is a low-level module, and 'executor'
2368
        # can not be imported at the begainning of this file.
2369 2370 2371 2372 2373
        # Therefore, the above two modules are dynamically imported.
        from .executor import global_scope

        if not (isinstance(value, np.ndarray) or hasattr(value, '__array__')):
            raise TypeError(
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                "`value` should be `numpy.ndarray` or `LoDTensor`, but received {}."
                .format(type(value)))
2376 2377 2378

        if scope is not None and not isinstance(scope, core._Scope):
            raise TypeError(
2379 2380
                "`scope` should be None or `paddle.static.Scope` type, but received {}."
                .format(type(scope)))
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        if scope is None:
            scope = global_scope()

        var_temp = scope.find_var(self.name)
        if var_temp is None:
            raise ValueError("Can not find Variable '{}' in the Scope.".format(
                self.name))

        t = var_temp.get_tensor()

        if hasattr(value, 'shape'):
            if isinstance(value.shape, (MethodType, FunctionType)):
                value_shape = value.shape()
            else:
                value_shape = value.shape
            if list(t.shape()) != list(value_shape):
                raise ValueError(
                    "{} expected a shape {}, but the received shape is {}.".
                    format(self.name, list(t.shape()), list(value_shape)))

        p = t._place()
        if p.is_cpu_place():
            place = core.CPUPlace()
        elif p.is_cuda_pinned_place():
            place = core.CUDAPinnedPlace()
        elif p.is_xpu_place():
            p = core.Place()
            p.set_place(t._place())
            place = core.XPUPlace(p.xpu_device_id())
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        elif p.is_npu_place():
            p = core.Place()
            p.set_place(t._place())
            place = core.NPUPlace(p.npu_device_id())
2415 2416 2417 2418
        elif p.is_mlu_place():
            p = core.Place()
            p.set_place(t._place())
            place = core.MLUPlace(p.mlu_device_id())
2419 2420 2421 2422 2423 2424 2425
        else:
            p = core.Place()
            p.set_place(t._place())
            place = core.CUDAPlace(p.gpu_device_id())

        t.set(value, place)

2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450
    def size(self):
        """
        Returns the number of elements for current Variable, which is a int64 Variable with shape [1]

        Returns:
            Variable: the number of elements for current Variable

        Examples:
            .. code-block:: python

                import paddle

                paddle.enable_static()

                # create a static Variable
                x = paddle.static.data(name='x', shape=[3, 2, 1])

                # get the number of elements of the Variable
                y = x.size()
        """

        output = self.block.create_var(
            name=unique_name.generate_with_ignorable_key(self.name + "_size"),
            dtype=core.VarDesc.VarType.INT64)

2451 2452 2453
        self.block.append_op(type='size',
                             inputs={'Input': [self]},
                             outputs={'Out': [output]})
2454 2455
        return output

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

    def _has_attr(self, name):
        """
        Whether this Variable has the attribute with the name `name` or not.

        Args:
            name(str): the attribute name.

        Returns:
            bool: True if has this attribute.
        """
        return self.desc.has_attr(name)

    def _remove_attr(self, name):
        self.desc.remove_attr(name)

    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(int|str|list): the value of the attribute.
        """
        self.desc._set_attr(name, val)

    @property
    def attr_names(self):
        """Get the names of all attributes defined."""
        return self.desc.attr_names()

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

        Args:
            name(str): the attribute name.

        Returns:
            int|str|list: The attribute value. The return value
            can be any valid attribute type.
        """
        return self.desc.attr(name)

    @property
    def process_mesh(self):
        """
        Get the process mesh belonging to this Variable.
        """
        from paddle.distributed.auto_parallel.interface import _g_process_mesh_map
        from paddle.distributed.auto_parallel.interface import ProcessMesh
        mesh_attr_name = 'mesh_id' + core.kAutoParallelSuffix()
        mesh_id = self.desc.attr(mesh_attr_name)
        return _g_process_mesh_map[mesh_id]

    @property
    def shard_mask(self):
        """
        Get shard_mask belonging to this Variable.
        """
        mask_attr_name = 'mask' + core.kAutoParallelSuffix()
        return self.desc.attr(mask_attr_name)

    @property
    def offload_device(self):
        """
        Get the offload device of this Variable.
        """
        offload_attr_name = 'offload_device' + core.kAutoParallelSuffix()
        return self.desc.attr(offload_attr_name)

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def get_all_op_protos():
    """
    Get all registered op proto from PaddlePaddle C++ end.
2540

2541 2542
    Returns:
       list: list of OpProto.
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    """
    protostrs = core.get_all_op_protos()
    ret_values = []
    for pbstr in protostrs:
2547
        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):
2553 2554 2555 2556
    """
    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__,
2566
            '_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]

2585 2586
    def update_op_proto(self):
        op_protos = get_all_op_protos()
2587
        custom_op_names = []
2588 2589 2590
        for proto in op_protos:
            if proto.type not in self.op_proto_map:
                self.op_proto_map[proto.type] = proto
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                custom_op_names.append(proto.type)

        return custom_op_names
2594

2595 2596 2597 2598
    @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(),
2600
            core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
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            core.op_proto_and_checker_maker.kOpCreationCallstackAttrName(),
            core.op_proto_and_checker_maker.kOpDeviceAttrName()
2603 2604
        }

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class Operator(object):
2607
    """
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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.
2637 2638 2639 2640

    Examples:
        .. code-block:: python

2641
            import paddle.fluid as fluid
2642
            cur_program = fluid.Program()
2643 2644 2645 2646 2647
            cur_block = cur_program.current_block()
            # var1 += var2 + var3
            cur_block.append_op(type="sum",
                                inputs={"X": [var1, var2, var3]},
                                outputs={"Out": [var1]})
2648
    """
2649
    OP_WITHOUT_KERNEL_SET = {
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        'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
        'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
2652
        'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
2653 2654
        'gen_bkcl_id', 'c_gen_bkcl_id', 'gen_nccl_id', 'c_gen_nccl_id',
        'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream',
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        'queue_generator', 'dequeue', 'enqueue', 'heter_listen_and_serv',
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        'c_wait_comm', 'c_wait_compute', 'c_gen_hccl_id', 'c_comm_init_hccl',
2657
        'copy_cross_scope', 'c_gen_cncl_id'
2658
    }
2659

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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 _non_static_mode():
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            if type is None:
                raise ValueError(
2670
                    "`type` to initialized an Operator can not be None.")
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            self._type = type
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            self.attrs = attrs if attrs else {}
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        else:
            self.block = block
            self.desc = desc
            # note: not add self.attrs here:
            # https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
            op_attrs = attrs
            if op_attrs is None:
                op_attrs = dict()
            del attrs

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            # attr for static mode cuda graph
            self._cuda_graph_attr = _current_cuda_graph_mode

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            op_maker = core.op_proto_and_checker_maker

            if op_maker.kOpRoleAttrName() not in op_attrs:
2689 2690
                op_attrs[
                    op_maker.kOpRoleAttrName()] = self.block.program._op_role
2691 2692

            role_var_name = op_maker.kOpRoleVarAttrName()
2693 2694
            if len(self.block.program._op_role_var
                   ) != 0 and role_var_name not in op_attrs:
2695
                op_attrs[role_var_name] = self.block.program._op_role_var
2696 2697 2698 2699 2700

            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:
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                # NOTE(Aurelius84): prog.clone() will lead that var.op is always None,
                # we add this to fix the problem.
                for arg in self.desc.output_arg_names():
                    if block.has_var(arg) and block.var(arg).op is None:
                        block.var(arg).op = self
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                return
            if type is None:
                raise ValueError(
2709
                    "`type` to initialized an Operator can not be None.")
2710 2711
            else:
                callstack_var_name = op_maker.kOpCreationCallstackAttrName()
2712 2713 2714
                op_attrs[callstack_var_name] = []
                for frame in traceback.extract_stack():
                    op_attrs[callstack_var_name].append(
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                        '  File "{}", line {}, in {}'.format(
                            frame[0], frame[1], frame[2]))
                    op_attrs[callstack_var_name].append('    {}'.format(
                        frame[3]))
2719 2720 2721 2722 2723 2724 2725

            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()

2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736
            # set device for op with kernels, give warning for op without kernels
            # when force_cpu and device_guard are used at the same time, a warning will be given.
            # TODO(zhangting2020): when force_cpu is removed, clear warning below.
            if _current_device is not None:
                if self._has_kernel(type):
                    op_device = op_maker.kOpDeviceAttrName()
                    op_attrs[op_device] = _current_device
                else:
                    warnings.warn("The Op(%s) is not support to set device." %
                                  type)
                if 'force_cpu' in op_attrs:
2737
                    if (type == 'less_than' and op_attrs['force_cpu'] != None
2738 2739 2740 2741 2742
                        ) or op_attrs['force_cpu'] != False:
                        warnings.warn(
                            "The Attr(force_cpu) of Op(%s) will be deprecated in the future, "
                            "please use 'device_guard' instead. 'device_guard' has higher priority when they are "
                            "used at the same time." % type)
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            if _current_pipeline_stage is not None:
                pipeline_attr_name = 'pipeline_stage' + core.kAutoParallelSuffix(
                )
                self._update_desc_attr(pipeline_attr_name,
                                       _current_pipeline_stage)
2748

2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761
            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]
2762
                        if not isinstance(in_args, (list, tuple)):
2763 2764 2765 2766 2767 2768
                            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 = []
2769
                        for index, arg in enumerate(in_args):
2770 2771 2772 2773
                            if isinstance(arg, six.string_types):
                                in_arg_names.append(arg)
                            elif isinstance(arg, six.binary_type):
                                in_arg_names.append(arg.decode())
2774
                            elif isinstance(arg, (Variable, core.VarBase)):
2775
                                in_arg_names.append(cpt.to_text(arg.name))
2776
                            else:
2777 2778 2779 2780
                                raise TypeError(
                                    "The type of '%s' in operator %s should be "
                                    "one of [basestring(), str, Varibale] in python2, "
                                    "or one of [str, bytes, Variable] in python3."
2781 2782
                                    "but received : %s" %
                                    (in_proto.name, type, arg))
2783 2784 2785 2786 2787 2788 2789 2790 2791
                        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):
2792 2793 2794 2795
                        raise ValueError(
                            ("Incorrect setting for output(s) of "
                             "operator \"%s\", should set: [%s].") %
                            (type, m.name))
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                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:
2808 2809 2810 2811
                        if isinstance(arg, six.string_types):
                            out_arg_names.append(arg)
                        else:
                            out_arg_names.append(cpt.to_text(arg.name))
2812
                        # TODO(minqiyang): could we remove variable's op in static mode?
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                        if not _non_static_mode():
2814 2815 2816 2817
                            if isinstance(arg, six.string_types):
                                block.var(arg).op = self
                            else:
                                arg.op = self
2818 2819 2820 2821 2822 2823 2824
                    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
2825 2826
                    if (attr_name
                            not in op_attrs) or (op_attrs[attr_name] is None):
2827 2828 2829 2830
                        continue
                    attr_val = op_attrs[attr_name]
                    self._update_desc_attr(attr_name, attr_val)

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            # proto.attrs doesn't include ipu_index
            if core.is_compiled_with_ipu():
2833
                if global_ipu_index >= 0:
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                    self._update_desc_attr(ipu_index_attr_name,
                                           global_ipu_index)
2836
                if global_ipu_stage >= 0:
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                    self._update_desc_attr(ipu_stage_attr_name,
                                           global_ipu_stage)

2840 2841 2842 2843 2844
            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):
2846 2847
        return op_type not in self.OP_WITHOUT_KERNEL_SET

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    def to_string(self, throw_on_error):
2849
        """
2850 2851
        Get debug string.

2852
        Args:
2853 2854
            throw_on_error(bool): Whether to raise exception if self is not
                initialized.
2855

2856 2857
        Returns:
            str: The debug string.
2858 2859

        """
2860
        protostr = self.desc.serialize_to_string()
2861
        proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
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        return _debug_string_(proto, throw_on_error)

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

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

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

        Returns:
            string: The formatted Operator string.

        Examples:
            .. code-block:: python

            import paddle.fluid as fluid

            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            var = cur_block.create_var(name="X",
                                       shape=[-1, 23, 48],
                                       dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [var]},
                                outputs={"Out": [var]})
            print(new_op._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
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        ), "skip_op_callstack parameter's type is error, expect bool, received {}".format(
2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942
            type(skip_op_callstack))
        outputs_str = "{"
        for i in range(0, len(self.output_names)):
            outputs_str += "{name}=".format(name=self.output_names[i])
            o = self.output(self.output_names[i])
            outputs_str += "{value}".format(value=o)
            if i != len(self.output_names) - 1:
                outputs_str += ", "
        outputs_str += "}"

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

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

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

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

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

2943
            # it is bytes of serialized protobuf
2944 2945 2946 2947
            if is_compiled_with_cinn(
            ) and self.type == 'cinn_launch' and name == 'compilation_key':
                key = self.desc.attr(name)
                v = core.get_serialize_comile_key(key)
2948 2949 2950 2951 2952 2953 2954 2955 2956
                prog = Program()
                prog = prog.parse_from_string(v)
                s = prog._to_readable_code()
                lines = s.split('\n')
                value = '\n'.join(['      ' + line for line in lines])
                value = '\n' + value
            else:
                value = self.desc.attr(name)

2957 2958 2959
            a = "{name} = {value}".format(name=name,
                                          type=attr_type,
                                          value=value)
2960

2961 2962 2963 2964
            attrs_str += a
            if i != len(attr_names) - 1:
                attrs_str += ", "

2965
        from paddle.distributed.auto_parallel.dist_context import get_default_distributed_context
2966
        dist_context = get_default_distributed_context()
2967 2968
        dist_op = dist_context.get_dist_op_for_program(self)
        if dist_op is not None:
2969 2970
            attrs_str += ", {name} = {value}".format(name="dist_attr",
                                                     value=dist_op)
2971

2972 2973
        if outputs_str != "{}":
            op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
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2974 2975
                format(outputs=outputs_str, op_type=self.type,
                       inputs=inputs_str, attrs=attrs_str)
2976 2977 2978 2979 2980
        else:
            op_str = "{op_type}(inputs={inputs}, {attrs})".\
                format(op_type=self.type, inputs=inputs_str, attrs=attrs_str)
        return op_str

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2981
    def __str__(self):
2982
        return self._to_readable_code()
2983 2984 2985

    __repr__ = __str__

F
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2986 2987
    @property
    def type(self):
2988
        return self.desc.type()
F
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2989 2990

    def input(self, name):
2991
        r"""
2992
        Get the input arguments according to the input parameter name.
2993

2994 2995
        Args:
            name(str): The input parameter name.
2996

2997 2998 2999
        Returns:
            list: return the list of argument names that associated with \
                the specific parameter name.
3000
        """
F
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3001 3002
        return self.desc.input(name)

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3003
    def _rename_input(self, old_name, new_name):
3004 3005 3006 3007 3008 3009 3010 3011 3012 3013
        """
        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
        """
W
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        self.desc._rename_input(old_name, new_name)
T
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W
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3016
    def _rename_output(self, old_name, new_name):
3017 3018 3019 3020 3021 3022 3023 3024 3025 3026
        """
        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)
T
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3029 3030 3031 3032
    @property
    def input_names(self):
        return self.desc.input_names()

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3033 3034 3035 3036 3037 3038 3039 3040
    @property
    def input_arg_names(self):
        return self.desc.input_arg_names()

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

F
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3041
    def output(self, name):
3042
        r"""
3043
        Get output arguments by the output parameter name.
3044

3045 3046
        Args:
            name(str): The output parameter name.
3047

3048 3049 3050
        Returns:
            list: return the list of argument names associated with \
                the specific parameter name.
3051
        """
F
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3052 3053 3054 3055 3056 3057
        return self.desc.output(name)

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

3058 3059 3060 3061 3062 3063 3064 3065
    @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.")

F
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3066
    def has_attr(self, name):
3067
        """
3068 3069
        Whether this Operator has the attribute with name or not.

3070
        Args:
3071
            name(str): the attribute name.
3072

3073 3074
        Returns:
            bool: True if has this attribute.
3075 3076

        """
F
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3077 3078 3079
        return self.desc.has_attr(name)

    def attr_type(self, name):
3080
        """
3081
        Get the type of attribute by attribute's name.
3082

3083 3084
        Args:
            name(str): the attribute name.
3085

3086 3087
        Returns:
            core.AttrType: the attribute type.
3088
        """
F
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3089 3090
        return self.desc.attr_type(name)

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    def _set_attr(self, name, val):
3092 3093 3094 3095 3096 3097 3098 3099 3100 3101
        """
        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)

3104 3105 3106
    def _remove_attr(self, name):
        self.desc.remove_attr(name)

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3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117
    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):
3122
            self.desc.set_blocks_attr(name, [v.desc for v in val])
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3123 3124 3125 3126
        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):
3134
        """
3135 3136
        Get the attribute by name.

3137
        Args:
3138
            name(str): the attribute name.
3139

3140 3141
        Returns:
            bool|int|str|float|list: The attribute value. The return value
3142 3143
            can be any valid attribute type.
        """
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        return self.desc.attr(name)
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W
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3146
    def _block_attr_id(self, name):
3147
        """
G
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3148
        Get the block attribute's id by name.
3149

3150 3151
        Args:
            name(str): the attribute name.
3152

3153 3154
        Returns:
            int: the block index.
3155
        """
W
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        return self.desc._block_attr_id(name)
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3157

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3158
    def _block_attr(self, name):
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3159 3160 3161 3162 3163 3164 3165 3166 3167 3168
        """
        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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3170 3171 3172
        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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3174 3175 3176 3177 3178 3179 3180 3181 3182 3183
        """
        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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3185 3186 3187 3188 3189
            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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3191 3192 3193 3194 3195 3196 3197 3198 3199 3200
        """
        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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3204
        """
3205 3206 3207
        Get the attribute dict.

        Returns:
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3208
            dict: The Operator's attribute dict, name->attr.
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3209 3210 3211 3212
        """
        attr_names = self.attr_names
        attr_map = {}
        for n in attr_names:
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3213 3214
            attr_type = self.desc.attr_type(n)
            if attr_type == core.AttrType.BLOCK:
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3215
                attr_map[n] = self._block_attr(n)
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3216 3217 3218
                continue

            if attr_type == core.AttrType.BLOCKS:
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                attr_map[n] = self._blocks_attr(n)
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3220 3221 3222 3223
                continue

            attr_map[n] = self.attr(n)

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3224 3225
        return attr_map

3226 3227 3228
    def _is_optimize_op(self):
        op_maker = core.op_proto_and_checker_maker
        OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize
3229 3230 3231 3232

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

3233 3234 3235
        op_role = self.desc.attr(op_maker.kOpRoleAttrName())
        if op_role & int(OPTIMIZE):
            return True
3236 3237 3238 3239 3240 3241 3242 3243

        return False

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

        if not self.desc.has_attr(op_maker.kOpRoleAttrName()):
3244 3245
            return False

3246 3247 3248 3249 3250 3251
        op_role = self.desc.attr(op_maker.kOpRoleAttrName())
        if op_role & int(BACKWARD):
            return True

        return False

3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276
    @property
    def process_mesh(self):
        """
        Get the process mesh belonging to this Operator.
        """
        from paddle.distributed.auto_parallel.interface import _g_process_mesh_map
        mesh_attr_name = 'mesh_id' + core.kAutoParallelSuffix()
        mesh_id = self.attr(mesh_attr_name)
        return _g_process_mesh_map[mesh_id]

    def dims_mapping(self, name):
        """
        Get the dims_mapping for the op's var named `name`.
        """
        dims_mapping_attr_name = name + core.kAutoParallelSuffix()
        return self.attr(dims_mapping_attr_name)

    @property
    def pipeline_stage(self):
        """
        Get pipeline stage of the Operator.
        """
        pipeline_stage_attr_name = 'pipeline_stage' + core.kAutoParallelSuffix()
        return self.desc.attr(pipeline_stage_attr_name)

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class Block(object):
3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292
    """
    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
W
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3293
        use `Program._create_block()` to create a block.
3294 3295 3296 3297

    Examples:
        .. code-block:: python

3298 3299 3300
            import paddle.fluid as fluid

            cur_program = fluid.Program()
3301 3302 3303 3304 3305 3306 3307 3308 3309
            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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3311
        self.desc = program.desc.block(idx)
3312
        self.vars = collections.OrderedDict()  # var_name --> var
Q
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3313
        self.ops = list()  # operator list
Y
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3314
        self.program = program
3315
        self.removed_vars = collections.OrderedDict()
Y
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3317
    def __str__(self):
3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351
        return self._to_readable_code()

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

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

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

        Returns:
            string: The formatted Block string.

        Examples:
            .. code-block:: python

            import paddle.fluid as fluid

            cur_program = fluid.Program()
            cur_block = cur_program.current_block()
            new_var = cur_block.create_var(name="X",
                                           shape=[-1, 23, 48],
                                           dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [new_var]},
                                outputs={"Out": [new_var]})
            print(cur_block._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
Z
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        ), "skip_op_callstack parameter's type is error, expect bool, received {}".format(
3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363
            type(skip_op_callstack))
        block_str = "{ // block "
        block_str += "{}\n".format(self.idx)
        for var in list(self.vars.values()):
            block_str += "    {}\n".format(var._to_readable_code())
        block_str += "\n"
        for op in self.ops:
            block_str += "    {}\n".format(
                op._to_readable_code(skip_op_callstack))
        block_str += "}"
        return block_str
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3365 3366
    def to_string(self, throw_on_error, with_details=False):
        """
3367 3368
        Get debug string.

F
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3369 3370
        Args:
            throw_on_error(bool): raise exception when self is not initialized
3371
                when throw_on_error is True.
F
update  
fengjiayi 已提交
3372
            with_details(bool): more details about variables and parameters
3373 3374
                (e.g. trainable, optimize_attr, ...) will be printed when
                with_details is True. Default False.
F
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3375

3376 3377
        Returns:
            str: The debug string.
F
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3378
        """
3379 3380
        assert isinstance(throw_on_error, bool) and isinstance(
            with_details, bool)
F
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3381
        if with_details:
F
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3382
            re_add_indent = re.compile(r"\n(.)")
F
fengjiayi 已提交
3383 3384
            res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
                self.idx, self.parent_idx)
3385
            for var in list(self.vars.values()):
F
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3386
                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
F
update  
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3387
                    r"\n    \1", var.to_string(throw_on_error, with_details))
F
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3388
            for op in self.ops:
F
fengjiayi 已提交
3389 3390
                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                    r"\n    \1", op.to_string(throw_on_error))
F
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3391 3392 3393
            res_str += "\n}"
        else:
            protostr = self.desc.serialize_to_string()
3394 3395
            proto = framework_pb2.BlockDesc.FromString(
                six.binary_type(protostr))
F
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3396 3397
            res_str = _debug_string_(proto, throw_on_error)
        return res_str
3398 3399 3400

    __repr__ = __str__

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3401 3402
    @property
    def parent_idx(self):
Y
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3403
        return self.desc.parent
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3405 3406 3407 3408
    @property
    def forward_block_idx(self):
        return self.desc.get_forward_block_idx()

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3409
    def _set_forward_block_idx(self, idx):
3410 3411 3412 3413 3414 3415 3416 3417 3418
        """
        Set the forward block Idx.

        Args:
            idx(int): the block index.

        Returns:
            None
        """
W
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3419
        self.desc._set_forward_block_idx(idx)
Y
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3421 3422 3423 3424 3425 3426 3427 3428
    @property
    def backward_block_idx(self):
        cur_block_idx = self.idx
        for block in self.program.blocks:
            if block.forward_block_idx == cur_block_idx:
                return block.idx
        return -1

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3429 3430
    @property
    def idx(self):
Y
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3431
        return self.desc.id
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3433
    def var(self, name):
3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446
        """
        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.
        """
3447
        if not isinstance(name, six.string_types):
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3448 3449 3450
            raise TypeError(
                "var require string as parameter, but get %s instead." %
                (type(name)))
Y
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3451 3452
        v = self.vars.get(name, None)
        if v is None:
Q
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3453
            raise ValueError("var %s not in this block" % name)
Y
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        return v
Q
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3455

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    def _find_var_recursive(self, name):
3457 3458 3459 3460 3461 3462 3463
        """
        Get a Variable by name from this block recursively.

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

        Returns:
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3464
            Variable: the Variable with the giving name. Or None if not found.
3465
        """
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3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489
        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))
X
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        return None
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3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510
    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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3512
    def all_parameters(self):
3513
        return list(self.iter_parameters())
3514

3515
    def iter_parameters(self):
M
minqiyang 已提交
3516
        return (item[1] for item in six.iteritems(self.vars)
3517
                if isinstance(item[1], Parameter))
Q
Qiao Longfei 已提交
3518

Y
Yu Yang 已提交
3519
    def create_var(self, *args, **kwargs):
J
Jiabin Yang 已提交
3520
        if _non_static_mode():
L
Leo Chen 已提交
3521 3522
            var = _varbase_creator(*args, **kwargs)
        else:
3523 3524 3525
            var = Variable(block=self, *args, **kwargs)
            if 'initializer' in kwargs:
                kwargs['initializer'](var, self)
Q
Qiao Longfei 已提交
3526
        return var
Y
Yu Yang 已提交
3527

Q
Qiao Longfei 已提交
3528 3529 3530
    def has_var(self, name):
        return name in self.vars

W
Wu Yi 已提交
3531
    def _rename_var(self, name, new_name):
T
typhoonzero 已提交
3532 3533
        """
        Rename variable in vars and ops' inputs and outputs
3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545

        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.
T
typhoonzero 已提交
3546
        """
M
minqiyang 已提交
3547 3548
        name = cpt.to_text(name)
        new_name = cpt.to_text(new_name)
M
minqiyang 已提交
3549

T
typhoonzero 已提交
3550
        if not self.has_var(name):
3551
            raise ValueError("var %s is not in current block" % name)
T
wip  
typhoonzero 已提交
3552 3553
        v = self.var(name)
        if type(v) == Parameter:
T
typhoonzero 已提交
3554
            var_type = "Parameter"
T
wip  
typhoonzero 已提交
3555 3556 3557 3558 3559 3560
            stop_gradient = v.stop_gradient
            trainable = v.trainable
            optimize_attr = v.optimize_attr
            regularizer = v.regularizer
            error_clip = v.error_clip
        elif type(v) == Variable:
T
typhoonzero 已提交
3561
            var_type = "Variable"
T
wip  
typhoonzero 已提交
3562 3563 3564 3565
            error_clip = v.error_clip
            stop_gradient = v.stop_gradient
        else:
            raise ValueError("unsupported var type: %s", type(v))
T
typhoonzero 已提交
3566
        orig_var_type = v.type
M
minqiyang 已提交
3567
        self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
W
Wu Yi 已提交
3568
        # NOTE: v is destroyed by C++ after calling _rename_var.
M
minqiyang 已提交
3569
        d = self.desc.find_var(cpt.to_bytes(new_name))
T
typhoonzero 已提交
3570
        if var_type == "Parameter":
L
Leo Chen 已提交
3571
            if in_dygraph_mode():
3572 3573 3574 3575 3576 3577 3578 3579 3580
                var = EagerParamBase(d.shape(),
                                     d.dtype(),
                                     type=orig_var_type,
                                     name=new_name,
                                     stop_gradient=stop_gradient,
                                     trainable=trainable,
                                     optimize_attr=optimize_attr,
                                     regularizer=regularizer,
                                     error_clip=error_clip)
3581
            else:
J
Jiabin Yang 已提交
3582
                if _in_legacy_dygraph():
3583 3584 3585 3586 3587 3588 3589 3590 3591
                    var = ParamBase(d.shape(),
                                    d.dtype(),
                                    type=orig_var_type,
                                    name=new_name,
                                    stop_gradient=stop_gradient,
                                    trainable=trainable,
                                    optimize_attr=optimize_attr,
                                    regularizer=regularizer,
                                    error_clip=error_clip)
J
Jiabin Yang 已提交
3592
                else:
3593 3594 3595 3596 3597 3598 3599 3600 3601 3602
                    var = Parameter(self,
                                    d.shape(),
                                    d.dtype(),
                                    type=orig_var_type,
                                    name=new_name,
                                    stop_gradient=stop_gradient,
                                    trainable=trainable,
                                    optimize_attr=optimize_attr,
                                    regularizer=regularizer,
                                    error_clip=error_clip)
T
typhoonzero 已提交
3603
        elif var_type == "Variable":
3604 3605 3606 3607 3608
            var = Variable(self,
                           type=orig_var_type,
                           name=new_name,
                           error_clip=error_clip,
                           stop_gradient=stop_gradient)
T
wip  
typhoonzero 已提交
3609

W
Wu Yi 已提交
3610
        # rename the python side, _sync_with_cpp will only add
T
wip  
typhoonzero 已提交
3611 3612 3613
        # new vars/ops to python side.
        self.vars[new_name] = var
        del self.vars[name]
W
Wu Yi 已提交
3614
        self._sync_with_cpp()
3615
        return var
T
typhoonzero 已提交
3616

3617 3618 3619
    def _remove_var(self, name, sync=True):
        if sync == True:
            self._sync_with_cpp()
M
minqiyang 已提交
3620
        self.desc._remove_var(cpt.to_bytes(name))
3621 3622
        del self.vars[name]

Y
Yu Yang 已提交
3623 3624
    def create_parameter(self, *args, **kwargs):
        global_block = self.program.global_block()
3625
        param = None
L
Leo Chen 已提交
3626
        if in_dygraph_mode():
J
Jiabin Yang 已提交
3627
            param = EagerParamBase(*args, **kwargs)
L
Leo Chen 已提交
3628
        else:
J
Jiabin Yang 已提交
3629 3630 3631 3632
            if _in_legacy_dygraph():
                param = ParamBase(*args, **kwargs)
            else:
                param = Parameter(global_block, *args, **kwargs)
3633

3634
        if 'initializer' in kwargs:
3635 3636 3637 3638 3639

            def _is_inited_by(block, var):
                init_ops = []
                for op in block.ops:
                    if var.name in op.output_arg_names:
3640
                        # In startup_program, "c_broadcast" and "c_sync_comm_stream"
T
tangwei12 已提交
3641
                        # are treated as initialization ops that cause error.
3642
                        # Think of "c_broadcast" and "c_sync_comm_stream" as a special case here.
3643 3644 3645 3646 3647
                        # NOTE: "coalesce_tensor" is a special case for rnn with cudnn support
                        if op.type in [
                                "c_broadcast", "c_sync_comm_stream",
                                "coalesce_tensor"
                        ]:
3648
                            continue
3649 3650 3651 3652 3653 3654 3655 3656
                        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 +
3657 3658
                                   " is inited by multiple init ops " +
                                   str(init_ops))
3659
            elif init_ops_len == 1:
3660
                # TODO already inited, do nothing, should log a warning
3661 3662 3663
                pass
            else:
                initializer(param, self)
Q
Qiao Longfei 已提交
3664
        return param
Y
Yu Yang 已提交
3665

Y
Yu Yang 已提交
3666
    def append_op(self, *args, **kwargs):
3667 3668 3669 3670 3671 3672
        """
        Appends a new Operator according to the giving arguments.

        Returns:
            Operator: the append Operator.
        """
J
Jiabin Yang 已提交
3673
        if _non_static_mode():
3674
            attrs = kwargs.get("attrs", {})
Z
zyfncg 已提交
3675
            inplace_map = kwargs.get("inplace_map", None)
J
Jiabin Yang 已提交
3676
            type = kwargs.get("type", None)
3677 3678 3679 3680
            warnings.warn(
                "Op `%s` is executed through `append_op` under the dynamic mode, "
                "the corresponding API implementation needs to be upgraded to "
                "using `_C_ops` method." % type, DeprecationWarning)
3681 3682 3683 3684 3685 3686
            op = Operator(block=self,
                          desc=None,
                          type=type,
                          inputs=None,
                          outputs=None,
                          attrs=attrs)
3687

M
minqiyang 已提交
3688 3689 3690
            # record ops in tracer rather than blocks
            #
            # TODO(minqiyang): add op stop_gradient support in static mode too.
L
lujun 已提交
3691
            # currently, we only support stop_gradient in dygraph mode.
J
Jiabin Yang 已提交
3692

3693 3694 3695
            _dygraph_tracer().trace_op(type, kwargs.get("inputs", {}),
                                       kwargs.get("outputs",
                                                  {}), attrs if attrs else {},
Z
zyfncg 已提交
3696 3697
                                       kwargs.get("stop_gradient", False),
                                       inplace_map)
M
minqiyang 已提交
3698
        else:
3699 3700
            from paddle.fluid.dygraph.base import param_guard

3701
            op_desc = self.desc.append_op()
3702 3703 3704 3705 3706 3707
            # NOTE(Aurelius84): In case of @to_static, all VarBase(s) should
            # be converted into Variable(s) with same name and block location.
            # This is ONE and ONLY logic of type transformation of dy2static.
            inputs = kwargs.get("inputs", None)
            outputs = kwargs.get("outputs", None)
            with param_guard(inputs), param_guard(outputs):
3708 3709 3710 3711 3712 3713
                op = Operator(block=self,
                              desc=op_desc,
                              type=kwargs.get("type", None),
                              inputs=inputs,
                              outputs=outputs,
                              attrs=kwargs.get("attrs", None))
3714

M
minqiyang 已提交
3715
            self.ops.append(op)
M
minqiyang 已提交
3716

3717 3718
        return op

W
Wu Yi 已提交
3719
    def _insert_op(self, index, *args, **kwargs):
3720 3721 3722 3723 3724 3725 3726 3727 3728
        """
        Insert a Operator according to the giving arguments.

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

        Returns:
            Operator: the insert Operator.
        """
W
Wu Yi 已提交
3729
        self._sync_with_cpp()
F
fangshuixun007 已提交
3730
        return self._insert_op_without_sync(index, *args, **kwargs)
Q
qiaolongfei 已提交
3731

3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748
    def _insert_op_without_sync(self, index, *args, **kwargs):
        """
        Insert an Operator according to the giving arguments, 
        without sync_with_cpp to meke the compilation faster.

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

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

    def _remove_op(self, index, sync=True):
3749 3750 3751 3752 3753 3754 3755 3756 3757
        """
        Remove the specific position operator.

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

        Returns:
            None
        """
3758 3759
        if sync == True:
            self._sync_with_cpp()
W
Wu Yi 已提交
3760
        self.desc._remove_op(index, index + 1)
3761 3762
        del self.ops[index]

W
Wu Yi 已提交
3763
    def _slice_ops(self, start, end):
3764 3765 3766 3767 3768 3769 3770 3771 3772 3773
        """
        Return the Operator between start and end.

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

        Returns:
            list: the Operators between start and end.
        """
Q
qiaolongfei 已提交
3774
        return self.ops[start:end]
Y
Yancey1989 已提交
3775

W
Wu Yi 已提交
3776
    def _prepend_op(self, *args, **kwargs):
J
Jiabin Yang 已提交
3777
        if _non_static_mode():
J
Jiabin Yang 已提交
3778 3779
            type = kwargs.get("type", None)
            attrs = kwargs.get("attrs", {})
3780 3781 3782 3783 3784 3785 3786 3787 3788 3789
            op = Operator(self,
                          None,
                          type=type,
                          inputs=None,
                          outputs=None,
                          attrs=attrs)

            _dygraph_tracer().trace_op(type, kwargs.get("inputs", {}),
                                       kwargs.get("outputs", {}),
                                       attrs if attrs else {},
M
minqiyang 已提交
3790
                                       kwargs.get("stop_gradient", False))
M
minqiyang 已提交
3791
        else:
3792
            op_desc = self.desc._prepend_op()
3793 3794 3795 3796 3797 3798
            op = Operator(self,
                          op_desc,
                          type=kwargs.get("type", None),
                          inputs=kwargs.get("inputs", None),
                          outputs=kwargs.get("outputs", None),
                          attrs=kwargs.get("attrs", None))
M
minqiyang 已提交
3799
            self.ops.insert(0, op)
3800

Y
Yu Yang 已提交
3801 3802
        return op

W
Wu Yi 已提交
3803
    def _sync_with_cpp(self):
3804
        """
3805 3806
        Sync from the desc on the c++ end. This method is used to synchronize
        the c++ desc instance generated by backward.
3807
        """
Q
Qiao Longfei 已提交
3808 3809 3810
        # sync variables from cpp
        for var in self.desc.all_vars():
            if not self.has_var(var.name()):
3811 3812 3813 3814
                is_stop_gradient = False
                if var.has_stop_gradient():
                    is_stop_gradient = var.stop_gradient()
                if var.has_is_parameter() and var.is_parameter():
3815 3816 3817 3818 3819 3820
                    self.create_parameter(name=var.name(),
                                          desc=var,
                                          type=var.type(),
                                          shape=var.shape(),
                                          dtype=var.dtype(),
                                          stop_gradient=is_stop_gradient)
3821
                else:
3822 3823 3824 3825
                    self.create_var(name=var.name(),
                                    desc=var,
                                    type=var.type(),
                                    stop_gradient=is_stop_gradient)
Q
Qiao Longfei 已提交
3826

3827
        # sync variables removed from c++ end
3828
        for var in list(self.vars.keys()):
M
minqiyang 已提交
3829
            if not self.desc.find_var(cpt.to_bytes(var)):
3830 3831
                self.vars.pop(var)

Q
Qiao Longfei 已提交
3832
        # sync operators from cpp
3833 3834 3835 3836
        ops_in_cpp = []
        for op_idx in range(0, self.desc.op_size()):
            ops_in_cpp.append(self.desc.op(op_idx))

Y
Yu Yang 已提交
3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852
        if len(self.ops) != 0:
            first_op_in_python = self.ops[0].desc
            last_op_in_python = self.ops[len(self.ops) - 1].desc
            start_index = None
            end_index = None
            for index in range(len(ops_in_cpp)):
                if first_op_in_python == ops_in_cpp[index]:
                    start_index = index
                if last_op_in_python == ops_in_cpp[index]:
                    end_index = index
            assert start_index is not None
            assert end_index is not None
            assert start_index <= end_index
        else:
            start_index = 0
            end_index = -1
Q
Qiao Longfei 已提交
3853 3854 3855 3856 3857

        # sync ops append to the head of cpp_ops
        for index in range((start_index - 1 - 1), -1, -1):
            op_desc = ops_in_cpp[index]
            op = Operator(self, op_desc)
Q
qiaolongfei 已提交
3858
            self.ops.insert(0, op)
Q
Qiao Longfei 已提交
3859 3860 3861 3862 3863 3864 3865

        # 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)

3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878
        # sync ops removed from c++ end
        if end_index != -1 and end_index < len(self.ops):
            ops_in_cpp_index = 0
            ops_in_python_index = 0
            while ops_in_python_index < len(
                    self.ops) and ops_in_cpp_index < len(ops_in_cpp):
                if self.ops[ops_in_python_index].desc != ops_in_cpp[
                        ops_in_cpp_index]:
                    del self.ops[ops_in_python_index]
                else:
                    ops_in_cpp_index += 1
                    ops_in_python_index += 1

Q
Qiao Longfei 已提交
3879 3880 3881 3882
        assert len(self.ops) == len(ops_in_cpp)
        for index in range(len(self.ops)):
            assert self.ops[index].desc == ops_in_cpp[index]

W
Wu Yi 已提交
3883
    def _copy_param_info_from(self, other):
3884
        """
3885 3886
        Copy the information of parameters from the other block.

3887
        Args:
3888 3889 3890 3891 3892
            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.
3893 3894 3895 3896 3897

        Returns:
            None
        """
        if not isinstance(other, Block):
W
Wu Yi 已提交
3898 3899
            raise TypeError(
                "_copy_param_info_from should be invoked with Block")
3900
        for p in other.iter_parameters():
3901 3902 3903
            assert isinstance(p, Parameter)
            v = self.vars.get(p.name, None)
            if v is None:
3904 3905
                # if the Parameter is pruned, v may be None
                continue
3906
            assert isinstance(v, Variable)
3907
            new_p = None
L
Leo Chen 已提交
3908
            if in_dygraph_mode():
3909 3910 3911 3912 3913 3914 3915 3916 3917 3918
                new_p = EagerParamBase(shape=v.shape,
                                       dtype=v.dtype,
                                       type=v.type,
                                       lod_level=v.lod_level,
                                       stop_gradient=p.stop_gradient,
                                       trainable=p.trainable,
                                       optimize_attr=p.optimize_attr,
                                       regularizer=p.regularizer,
                                       error_clip=p.error_clip,
                                       name=v.name)
3919
            else:
J
Jiabin Yang 已提交
3920
                if _in_legacy_dygraph():
3921 3922 3923 3924 3925 3926 3927 3928 3929 3930
                    new_p = ParamBase(shape=v.shape,
                                      dtype=v.dtype,
                                      type=v.type,
                                      lod_level=v.lod_level,
                                      stop_gradient=p.stop_gradient,
                                      trainable=p.trainable,
                                      optimize_attr=p.optimize_attr,
                                      regularizer=p.regularizer,
                                      error_clip=p.error_clip,
                                      name=v.name)
J
Jiabin Yang 已提交
3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944
                else:
                    new_p = Parameter(
                        block=self,
                        shape=v.shape,
                        dtype=v.dtype,
                        type=v.type,
                        lod_level=v.lod_level
                        if v.type == core.VarDesc.VarType.LOD_TENSOR else None,
                        stop_gradient=p.stop_gradient,
                        trainable=p.trainable,
                        optimize_attr=p.optimize_attr,
                        regularizer=p.regularizer,
                        error_clip=p.error_clip,
                        name=v.name)
3945 3946
            self.vars[new_p.name] = new_p

3947
    def _clone_variable(self, var, force_persistable=True):
3948 3949
        """
        Clone a variable into current block.
3950

3951 3952
        Args:
            var: the variable to be cloned.
3953 3954 3955
            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.
3956 3957

        Returns:
3958
            Variable: the new  variable cloned from 'var' in current block.
3959 3960
        """
        assert isinstance(var, Variable)
T
update  
typhoonzero 已提交
3961 3962 3963
        ret_var = None
        # make STEP_SCOPES var can be safely cloned.
        if var.type == core.VarDesc.VarType.STEP_SCOPES:
3964 3965 3966
            ret_var = self.create_var(name=var.name,
                                      persistable=var.persistable,
                                      type=var.type)
T
tangwei12 已提交
3967
        elif var.type == core.VarDesc.VarType.RAW:
3968 3969 3970
            ret_var = self.create_var(name=var.name,
                                      persistable=var.persistable,
                                      type=var.type)
T
typhoonzero 已提交
3971 3972 3973 3974 3975 3976
        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,
3977
                persistable=True if force_persistable else var.persistable,
H
Huihuang Zheng 已提交
3978 3979
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
typhoonzero 已提交
3980 3981 3982 3983 3984 3985 3986
        else:
            ret_var = self.create_var(
                name=var.name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
3987
                persistable=True if force_persistable else var.persistable,
H
Huihuang Zheng 已提交
3988 3989
                is_data=var.is_data,
                need_check_feed=var.desc.need_check_feed())
T
update  
typhoonzero 已提交
3990
        return ret_var
3991

Y
Yu Yang 已提交
3992

3993 3994 3995 3996
# NOTE(zjl): you should be careful that after you call this method,
# some Python Variable and all Python Operators should not be used
# again. Because all Python Variables and all Python Operators are
# re-constructed inside this method. The underlying VarDesc(OpDesc)
3997
# of some old Python Variables(all old Python Operators) may have
3998
# been destructed.
3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014
def _apply_pass(main_program,
                startup_program,
                pass_name,
                pass_attrs={},
                pass_attr_types={}):
    assert isinstance(pass_attrs, dict), "pass_attrs must be dict"
    assert isinstance(pass_attr_types, dict), "pass_attr_types must be dict"
    tmp_main_program = core.ProgramDesc(main_program.desc)
    tmp_startup_program = core.ProgramDesc(startup_program.desc)
    attrs = core.apply_pass(tmp_main_program, tmp_startup_program, pass_name,
                            pass_attrs, pass_attr_types)
    main_program._rebuild_from_desc(tmp_main_program)
    startup_program._rebuild_from_desc(tmp_startup_program)
    return attrs


4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109
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()

4110
    def remove_input_by_id(self, node_id):
4111 4112 4113 4114 4115 4116
        """
        Remove a node from inputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
4117
        self.node.remove_input(node_id)
4118

4119
    def remove_input(self, node):
4120 4121 4122 4123
        """
        Remove a node from inputs.

        Args:
4124
            node(IrNode): the node being removed.
4125
        """
4126
        self.node.remove_input(node.node)
4127

4128
    def append_input(self, node):
4129 4130 4131 4132
        """
        Append a node in inputs.

        Args:
4133
            node(IrNode): the node being appended.
4134
        """
4135
        self.node.append_input(node.node)
4136 4137 4138 4139 4140 4141 4142 4143

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

4144
    def remove_output_by_id(self, node_id):
4145 4146 4147 4148 4149 4150
        """
        Remove a node from outputs by the given node id.

        Args:
            node_id(int): the given node id.
        """
4151
        self.node.remove_output(node_id)
4152

4153
    def remove_output(self, node):
4154 4155 4156 4157
        """
        Remove a node from outputs.

        Args:
4158
            node(IrNode): the node being removed.
4159
        """
4160
        self.node.remove_output(node.node)
4161

4162
    def append_output(self, node):
4163 4164 4165 4166
        """
        Append a node in outputs.

        Args:
4167
            node(IrNode): the node being appended.
4168
        """
4169
        self.node.append_output(node.node)
4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 4214 4215 4216

    @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, \
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4217
            "The node variable description can not be None."
4218 4219 4220 4221 4222 4223 4224 4225 4226 4227
        self.node.var().set_shape(shape)

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

        Returns:
            bool: indicate whether the variable is persistable.
        """
        assert self.node.var() is not None, \
T
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4228
            "The node variable description can not be None."
4229 4230
        return self.node.var().persistable()

4231 4232 4233 4234 4235 4236 4237 4238
    def type(self):
        """
        Return the variable type.

        Returns:
            core.VarDesc.VarType: the variable type.
        """
        assert self.node.var() is not None, \
T
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4239
            "The node variable description can not be None."
4240 4241 4242 4243 4244 4245 4246 4247 4248 4249
        return self.node.var().type()

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

        Returns:
            core.VarDesc.VarType: the variable data type.
        """
        assert self.node.var() is not None, \
T
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4250
            "The node variable description can not be None."
4251 4252 4253 4254 4255 4256 4257 4258 4259 4260
        return self.node.var().dtype()

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

        Returns:
            list: the variable shape.
        """
        assert self.node.var() is not None, \
T
tianshuo78520a 已提交
4261
            "The node variable description can not be None."
4262 4263
        return self.node.var().shape()

4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310
    @property
    def inputs(self):
        """
        Return the node inputs.

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

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

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


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

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

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

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

        Args:
            old_input_name(str): the old input name.
            new_input_name(str): the new input name.
        """
        assert self.node.op() is not None, \
T
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4311
            "The node operator description can not be None."
4312 4313
        self.node.op()._rename_input(old_input_name, new_input_name)

4314 4315 4316 4317 4318 4319 4320 4321 4322
    def rename_output(self, old_output_name, new_output_name):
        """
        Rename the output of this node.

        Args:
            old_output_name(str): the old output name.
            new_output_name(str): the new output name.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4323
            "The node operator description can not be None."
4324 4325
        self.node.op()._rename_output(old_output_name, new_output_name)

4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336
    def input(self, name):
        """
        Get the argument name list by the parameter name for input.

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4337
            "The node operator description can not be None."
4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350
        return self.node.op().input(name)

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

        Args:
            name(str): the parameter name.

        Returns:
            list(str): the argument name list.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4351
            "The node operator description can not be None."
4352 4353 4354 4355 4356 4357 4358 4359 4360 4361
        return self.node.op().output(name)

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

        Args:
            new_type(str): new operator type to be set.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4362
            "The node operator description can not be None."
4363 4364
        return self.node.op().set_type(new_type)

4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379
    def set_attr(self, name, val):
        """
        Set the value of attribute by attribute's name.

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

    def _update_desc_attr(self, name, val):
        """
        Update the value of the op desc's attribute by attribute's name.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4380
            "The node operator description can not be None."
4381 4382 4383 4384
        desc = self.node.op()
        if isinstance(val, Block):
            desc.set_block_attr(name, val.desc)
        elif isinstance(val, list) and val and \
4385
                all(isinstance(v, Block) for v in val):
4386 4387
            desc.set_blocks_attr(name, [v.desc for v in val])
        elif isinstance(val, core.BlockDesc) or \
4388
                isinstance(val, core.ProgramDesc):
4389 4390 4391 4392
            desc.set_serialized_attr(name, val.serialize_to_string())
        else:
            desc._set_attr(name, val)

4393 4394 4395 4396 4397 4398 4399 4400
    def input_arg_names(self):
        """
        Return input arguments' names of this op node.

        Returns:
            list(str): input arguments' names of this op node.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4401
            "The node operator description can not be None."
4402 4403 4404 4405 4406 4407 4408 4409 4410 4411
        return self.node.op().input_arg_names()

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

        Returns:
            list(str): output arguments' names of this op node.
        """
        assert self.node.op() is not None, \
T
tianshuo78520a 已提交
4412
            "The node operator description can not be None."
4413 4414
        return self.node.op().output_arg_names()

4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435
    @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]


4436 4437
class IrGraph(object):
    """
4438
    Python IrGraph. Beneath it is a core.Graph, which is used for
4439
    creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
4440 4441
    a Program. In an IrGraph, both Variables and Operators are graph
    nodes.
4442 4443 4444 4445
    """

    def __init__(self, graph, for_test=False):
        """
4446 4447
        Construct an IrGraph using core.Graph.

4448 4449 4450 4451 4452 4453 4454 4455 4456
        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

4457 4458 4459 4460
    def clone(self):
        """
        Create a new and duplicated IrGraph.

4461 4462 4463
        Warns:
            The method only clones the graph structure, not its attributes.

4464 4465 4466
        Returns:
            IrGraph: A new and duplicated graph.
        """
4467
        g = self.graph.clone()
4468 4469
        return IrGraph(g, self._for_test)

4470
    def is_test(self):
4471 4472 4473
        """
        If the graph is used for testing, the function returns true. Otherwise, returns false.
        """
4474 4475
        return self._for_test

W
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4476
    def all_nodes(self):
4477 4478 4479
        """
        Return all nodes included in the graph as a set.
        """
4480
        return {IrNode(node) for node in self.graph.nodes()}
4481

4482
    def all_var_nodes(self):
4483 4484 4485
        """
        Return all variable nodes included in the graph as a set.
        """
4486
        return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
4487

4488
    def all_persistable_nodes(self):
4489 4490 4491
        """
        Return all persistable variable nodes included in the graph as a set.
        """
W
WangZhen 已提交
4492 4493 4494 4495 4496
        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)
4497
        return {IrVarNode(p) for p in persistable_nodes}
W
WangZhen 已提交
4498

4499
    def all_op_nodes(self):
4500 4501 4502
        """
        Return all operator nodes included in the graph as a set.
        """
4503
        return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
4504

4505 4506 4507 4508 4509 4510
    def all_sub_graphs(self, for_test=False):
        """
        Return all sub_graphs included in the main graph as a set.
        """

        return [
4511
            IrGraph(self.graph.get_sub_graph(i), for_test=for_test)
4512 4513 4514 4515 4516 4517 4518 4519 4520
            for i in range(self.graph.sub_graph_size())
        ]

    def get_sub_graph(self, i, for_test=False):
        """
        Return i-th sub_graph in the main graph.
        """
        return IrGraph(self.graph.get_sub_graph(i), for_test=for_test)

4521
    def create_persistable_node(self, name, var_type, shape, var_dtype):
4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 4532
        """
        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:
4533
            IrVarNode: the created persistable variable node.
4534
        """
4535 4536 4537 4538 4539
        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)
4540
        return IrVarNode(self.graph.create_var_node(var_desc))
4541 4542

    def create_var_node(self, name, var_type, shape, var_dtype):
4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553
        """
        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:
4554
            IrVarNode: the created variable node.
4555 4556
        """

4557 4558 4559 4560
        var_desc = core.VarDesc(name)
        var_desc.set_type(var_type)
        var_desc.set_shape(shape)
        var_desc.set_dtype(var_dtype)
4561
        return IrVarNode(self.graph.create_var_node(var_desc))
4562

4563 4564 4565 4566 4567 4568
    def create_control_dep_var(self):
        """
        create a control var
        """
        return IrVarNode(self.graph.create_control_dep_var())

4569
    def create_var_node_from_desc(self, var_desc):
4570 4571 4572 4573 4574 4575 4576 4577
        """
        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:
4578
            IrVarNode: the created variable node.
4579
        """
4580
        return IrVarNode(self.graph.create_var_node(var_desc))
4581 4582

    def create_op_node(self, op_type, attrs, inputs, outputs):
4583 4584 4585 4586 4587 4588 4589
        """
        Create a operator node in the graph.

        Args:
            op_type(str): the type of the operator node.
            attrs(dict): the attributes of the operator node.
            inputs(dict): the inputs of the operator node.
T
tianshuo78520a 已提交
4590
            outputs(dict): the outputs of the operator node.
4591 4592

        Returns:
4593
            IrOpNode: the created operator node.
4594
        """
4595 4596
        op_desc = core.OpDesc()
        op_desc.set_type(op_type)
4597
        for attr, value in six.iteritems(attrs):
4598
            self._update_desc_attr(op_desc, attr, value)
4599
        for input_name, var_nodes in six.iteritems(inputs):
4600 4601 4602 4603
            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])
4604
        for output_name, var_nodes in six.iteritems(outputs):
4605 4606 4607 4608
            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])
4609
        return IrOpNode(self.graph.create_op_node(op_desc))
4610 4611

    def create_op_node_from_desc(self, op_desc):
4612 4613 4614 4615 4616 4617 4618
        """
        Create a operator node by using an existing OpDesc in the graph.

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

        Returns:
4619
            IrOpNode: the created operator node.
4620
        """
4621
        return IrOpNode(self.graph.create_op_node(op_desc))
4622 4623

    def update_input_link(self, old_input_node, new_input_node, op_node):
4624 4625 4626 4627
        """
        Update the input's link of a operator node.

        Args:
4628 4629 4630
            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.
4631
        """
4632
        assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
T
tangwei12 已提交
4633
            self.graph.nodes() and op_node.node in self.graph.nodes(), \
4634
            'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
4635 4636 4637 4638
        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)
4639
        op_node.rename_input(old_input_node.name(), new_input_node.name())
4640

4641 4642 4643 4644 4645 4646 4647 4648 4649 4650
    def update_output_link(self, old_output_node, new_output_node, op_node):
        """
        Update the output's link of an operator node.

        Args:
            old_output_node(IrNode): the old output node of the giving op_node.
            new_output_node(IrNode): the new output node of the giving op_node.
            op_node(IrOpNode): the operator node that is needed to update input's link.
        """
        assert old_output_node.node in self.graph.nodes() and new_output_node.node in \
T
tangwei12 已提交
4651
            self.graph.nodes() and op_node.node in self.graph.nodes(), \
4652
            'The three arguments(old_output_node &new_output_node &op_node) must be in the graph nodes.'
4653 4654 4655 4656 4657 4658
        old_output_node.remove_input(op_node)
        op_node.remove_output(old_output_node)
        new_output_node.append_input(op_node)
        op_node.append_output(new_output_node)
        op_node.rename_output(old_output_node.name(), new_output_node.name())

4659
    def link_to(self, node_in, node_out):
4660 4661 4662 4663
        """
        Connect two nodes.

        Args:
4664 4665
            node_in(IrNode): the input node.
            node_out(IrNode): the output node.
4666
        """
4667 4668 4669 4670
        assert node_in.node in self.graph.nodes(), (
            'node_in(%s) must be in the graph nodes.' % node_in.node.name())
        assert node_out.node in self.graph.nodes(), (
            'node_out(%s) must be in the graph nodes.' % node_out.node.name())
4671 4672
        node_in.append_output(node_out)
        node_out.append_input(node_in)
4673 4674

    def safe_remove_nodes(self, remove_nodes):
4675 4676 4677 4678 4679 4680 4681
        """
        Remove nodes safely since links connected to these removed nodes are
        also removed.

        Args:
            remove_nodes(set): the nodes prepared to be removed.
        """
4682
        if not isinstance(remove_nodes, set):
W
WangZhen 已提交
4683 4684 4685 4686
            if isinstance(remove_nodes, Iterable):
                remove_nodes = set(remove_nodes)
            else:
                remove_nodes = {remove_nodes}
4687 4688
        original_nodes = {n.node for n in remove_nodes}
        core.graph_safe_remove_nodes(self.graph, original_nodes)
4689

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4690 4691 4692 4693 4694 4695 4696 4697
    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] = [
4698
                            self._find_node_by_name(node.inputs, each_var_name)
Z
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4699 4700 4701 4702
                        ]
                for each_var_name in node.op().output_arg_names():
                    if each_var_name not in var_nodes:
                        var_nodes[each_var_name] = [
4703
                            self._find_node_by_name(node.outputs, each_var_name)
Z
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4704 4705 4706
                        ]
                    else:
                        var_nodes[each_var_name].append(
4707 4708
                            self._find_node_by_name(node.outputs,
                                                    each_var_name))
Z
Zhen Wang 已提交
4709 4710
        self.graph.resolve_hazard(var_nodes)

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4711
    def has_circle(self):
4712 4713 4714 4715 4716 4717
        """
        Check if the graph has a circle.

        Returns:
            bool: True if the graph has a circle else False.
        """
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4718 4719 4720
        return core.has_circle(self.graph)

    def graph_num(self):
4721 4722 4723 4724 4725 4726
        """
        Count the number of unconnected graphs in this graph.

        Returns:
            int: the number of unconnected graphs.
        """
W
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4727 4728 4729
        return core.graph_num(self.graph)

    def topology_sort(self):
4730 4731 4732
        """
        Perform the topology sort operation on the graph.

T
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4733
        Notes: the `graph` can not contain a circle.
4734 4735

        Returns:
Z
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4736
            list(IrNode): nodes in topology order.
4737
        """
4738
        ordered_nodes = core.topology_sort(self.graph)
Z
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4739
        return [IrNode(n) for n in ordered_nodes]
W
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4740 4741

    def build_adjacency_list(self):
4742 4743 4744 4745
        """
        Build an adjacency list of operations for the `graph`.

        Returns:
4746
            dict{IrNode: set(IrNode)}: the adjacency list.
4747
        """
4748 4749 4750 4751 4752
        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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4754 4755 4756 4757 4758 4759 4760 4761
    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.
4762
            marked_nodes(set(IrNode)): nodes that are needed to be marked.
4763 4764 4765 4766 4767
            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.
        """

4768 4769
        def _convert_to_pdf(dot_file_path):
            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
4770 4771 4772
            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path +
                                          ' -o ' + pdf_save_path,
                                          shell=True)
4773 4774 4775 4776 4777
            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))

4778
        remove_ctr_vars = set()
4779
        if remove_ctr_var:
4780
            for node in self.all_var_nodes():
4781 4782 4783
                if node.is_ctrl_var():
                    remove_ctr_vars.add(node)
            self.safe_remove_nodes(remove_ctr_vars)
4784 4785
        print('Total ops num = {}.'.format(len(self.all_op_nodes())))

4786 4787
        if marked_nodes is not None:
            if not isinstance(marked_nodes, set):
4788 4789 4790 4791 4792 4793
                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}
4794 4795 4796 4797
            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)
4798 4799
        if not os.path.exists(save_path):
            os.makedirs(save_path)
4800 4801 4802 4803 4804 4805 4806
        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):
4807 4808 4809
        """
        Convert the graph into a Program.

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4810
        WARN: When the graph includes backward operator nodes, the
4811 4812 4813 4814 4815 4816
        conversion process may be failed. Usually, this function is
        only used to convert a test graph.

        Returns:
            Program: a program converted from the graph.
        """
4817
        convert_pass = core.get_pass('graph_to_program_pass')
4818 4819
        desc = core.ProgramDesc()
        convert_pass.set_not_owned('program', desc)
4820 4821 4822 4823
        convert_pass.apply(self.graph)
        program = Program._construct_from_desc(desc)
        return program

4824 4825 4826 4827 4828 4829 4830 4831
    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
4832 4833
        assert target_node is not None, (
            "Cannot find the target node (%s)in the giving set." % node_name)
4834 4835
        return target_node

4836 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851
    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):
D
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    """
4854
    Create Python Program.  It has at least one :ref:`api_guide_Block_en`, when the
4855
    control flow op like conditional_block, while :ref:`api_paddle_fluid_layers_While` is included,
J
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4856
    it will contain nested block.
4857

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4858 4859 4860
    Please reference the
    `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_
    for details.
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4862
    A set of Program usually contains startup program and main program.
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4863
    A startup program is set to contain some initial work, eg. initialize the ``Parameter``, and the main
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    program will contain the network structure and vars for train.

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

J
Jiabin Yang 已提交
4871
    **Notes**:
4872 4873 4874
        **we have** :ref:`api_paddle_fluid_framework_default_startup_program` **and** :ref:`api_paddle_fluid_framework_default_main_program`
        **by default, a pair of them will shared the parameters. The** :ref:`api_paddle_fluid_framework_default_startup_program` **only run once to initialize parameters,**
        :ref:`api_paddle_fluid_framework_default_main_program` **run in every mini batch and adjust the weights.**
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4875 4876

    Returns:
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4877
        Program: An empty Program.
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4878 4879

    Examples:
4880 4881
        .. code-block:: python

4882 4883 4884 4885
            import paddle
            import paddle.static as static

            paddle.enable_static()
4886

4887 4888 4889 4890 4891
            main_program = static.Program()
            startup_program = static.Program()
            with static.program_guard(main_program=main_program, startup_program=startup_program):
                x = static.data(name="x", shape=[-1, 784], dtype='float32')
                y = static.data(name="y", shape=[-1, 1], dtype='int32')
4892
                z = static.nn.fc(name="fc", x=x, size=10, activation="relu")
4893 4894 4895

            print("main program is: {}".format(main_program))
            print("start up program is: {}".format(startup_program))
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4896 4897 4898

    """

4899 4900
    def __init__(self):
        self.desc = core.ProgramDesc()
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4901 4902
        self.blocks = [Block(self, 0)]
        self.current_block_idx = 0
4903 4904
        global global_prog_seed
        self._seed = global_prog_seed
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        self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
4906
        self.__op_role_var = []
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4907

4908 4909
        # for distribute training
        # _is_distributed = True if under distributed training
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        self._is_distributed = False
4911
        # _is_chief = True if the trainer is the first one, usually No.0
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        self._is_chief = False
4913 4914 4915
        # _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"]
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        self._endpoints = []
4917 4918 4919
        # if current role is parameter server, the _ps_endpoint is its "ip:port"
        self._ps_endpoint = None
        # trainers_endpoints, it is used for distribution.
4920
        self._trainers_endpoints = []
4921
        # the distributed lookup table names
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4922
        self._distributed_lookup_table = None
4923 4924 4925

        # use Deep gradient comrepssion or not
        self._enable_dgc = False
4926 4927
        self._use_lamb = False

4928 4929 4930
        self._nccl_comm_num = 1
        self._use_hierarchical_allreduce = False
        self._hierarchical_allreduce_inter_nranks = 0
4931

4932 4933 4934
        # if this program has been optimized by distributed optimizer
        # fleet_opt will be given a value
        self._fleet_opt = None
D
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4935
        self._program_config = None
4936

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4937 4938 4939
        # assigned if this program has been parsed by a pipeline optimizer
        self._pipeline_opt = None

4940 4941 4942
        # assigned if this program has been parsed by a heter pipeline parameter server optimizer
        self._heter_pipeline_opt = None

4943 4944 4945
        # appending gradients times
        self._appending_grad_times = 0

4946 4947 4948 4949
        # identifier for auto checkpoint
        self._auto_checkpoint_name = unique_name.generate(
            "__auto_checkpoint_program__")

4950 4951
        # compiled program, i.e. Graph
        self._graph = None
4952 4953
        # to tag whether is startup_program
        self._is_start_up_program_ = False
4954

4955
    def _find_var_class_kwargs(self, new_desc):
4956 4957 4958 4959 4960 4961 4962 4963
        # NOTE: not all variables support shape/dtype/lod_level methods.
        # For example: RAW, STEP_SCOPES, etc.
        def get_var_desc_attr_or_none(var_desc, attr_name, allowed_types):
            if var_desc.type() in allowed_types:
                return getattr(var_desc, attr_name)()
            else:
                return None

4964 4965 4966 4967 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977
        old_desc = self.desc
        all_new_vars = []
        block_num = new_desc.num_blocks()
        for idx in range(block_num):
            new_block_desc = new_desc.block(idx)
            all_new_vars.append([])
            block_new_vars = all_new_vars[-1]
            for new_var_desc in new_block_desc.all_vars():
                if self.blocks[idx].has_var(new_var_desc.name()):
                    old_var = self.blocks[idx].var(new_var_desc.name())
                else:
                    old_var = None

                kwargs = {
4978 4979 4980 4981 4982 4983
                    'type':
                    new_var_desc.type(),
                    'name':
                    new_var_desc.name(),
                    'shape':
                    get_var_desc_attr_or_none(new_var_desc, "shape", [
4984 4985 4986 4987
                        core.VarDesc.VarType.LOD_TENSOR,
                        core.VarDesc.VarType.SELECTED_ROWS,
                        core.VarDesc.VarType.LOD_TENSOR_ARRAY,
                    ]),
4988 4989
                    'dtype':
                    get_var_desc_attr_or_none(new_var_desc, "dtype", [
4990 4991 4992 4993 4994 4995 4996 4997 4998
                        core.VarDesc.VarType.LOD_TENSOR,
                        core.VarDesc.VarType.SELECTED_ROWS,
                        core.VarDesc.VarType.LOD_TENSOR_ARRAY,
                    ]),
                    'lod_level':
                    get_var_desc_attr_or_none(new_var_desc, "lod_level", [
                        core.VarDesc.VarType.LOD_TENSOR,
                        core.VarDesc.VarType.LOD_TENSOR_ARRAY,
                    ]),
4999 5000 5001 5002 5003 5004 5005 5006 5007 5008
                    'error_clip':
                    old_var.error_clip if old_var is not None else None,
                    'stop_gradient':
                    old_var.stop_gradient if old_var is not None else False,
                    'is_data':
                    old_var.is_data if old_var is not None else False,
                    'need_check_feed':
                    new_var_desc.need_check_feed(),
                    'belong_to_optimizer':
                    old_var.belong_to_optimizer
5009 5010 5011 5012 5013 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038
                    if old_var is not None else False,
                }

                if isinstance(old_var, Parameter):
                    kwargs.update({
                        'trainable': old_var.trainable,
                        'optimize_attr': old_var.optimize_attr,
                        'regularizer': old_var.regularizer,
                        'do_model_average': old_var.do_model_average,
                        'need_clip': old_var.need_clip,
                        'is_distributed': old_var.is_distributed,
                        'is_parameter': old_var.is_parameter,
                    })
                    block_new_vars.append({
                        'class': Parameter,
                        'kwargs': copy.deepcopy(kwargs),
                    })
                else:
                    kwargs['persistable'] = new_var_desc.persistable()
                    block_new_vars.append({
                        'class': Variable,
                        'kwargs': copy.deepcopy(kwargs),
                    })

        return all_new_vars

    def _rebuild_from_desc(self, desc):
        all_new_vars = self._find_var_class_kwargs(desc)
        block_num = desc.num_blocks()
        assert block_num == len(all_new_vars)
5039
        assert block_num == self.desc.num_blocks()
5040 5041

        # clear old blocks and desc
5042 5043 5044 5045 5046 5047 5048 5049 5050
        for idx in range(block_num):
            block = self.blocks[idx]
            block.vars.clear()
            block.ops.clear()

        for idx in range(block_num):
            block_desc = self.blocks[idx].desc
            new_block_desc = desc.block(idx)
            block_desc._move_from(new_block_desc)
5051

5052
        del desc
5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070 5071

        # add new vars first
        for idx in range(block_num):
            block = self.blocks[idx]
            for new_var in all_new_vars[idx]:
                clazz = new_var['class']
                kwargs = new_var['kwargs']
                kwargs['block'] = block
                clazz(**kwargs)

        # then append op
        for idx in range(block_num):
            block = self.blocks[idx]
            block_desc = self.desc.block(idx)
            for op_idx in range(block_desc.op_size()):
                op_desc = block_desc.op(op_idx)
                op = Operator(block=block, desc=op_desc)
                block.ops.append(op)

5072 5073 5074 5075 5076 5077 5078 5079 5080 5081
    def global_seed(self, seed=0):
        """
        Set global seed for Program

        Returns:
            None.

        Examples:
            .. code-block:: python

5082 5083
                import paddle
                import paddle.static as static
5084

5085 5086 5087
                paddle.enable_static()

                prog = static.default_main_program()
5088 5089 5090 5091 5092
                print(prog.random_seed)
                ## 0
                ## the default random seed is 0

                prog.global_seed(102)
5093
                prog1 = static.default_main_program()
5094 5095 5096 5097 5098 5099 5100 5101
                print(prog1.random_seed)
                ## 102
                ## the random seed is 102
        """
        global global_prog_seed
        global_prog_seed = seed
        self._seed = global_prog_seed

Y
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5102
    @property
5103
    def _op_role(self):
Y
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5104 5105 5106 5107 5108 5109 5110 5111
        """
        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
5112
        parameter gradient of backward (use :code:`_op_role_var` to get this
Y
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5113 5114 5115 5116
        variable) operator should be merged to one device. The optimization
        operators should be executed on only one device and broadcast the
        optimization result, i.e., the new parameter, to every other device.
        """
Y
yuyang18 已提交
5117 5118
        return self._current_role

5119 5120
    @_op_role.setter
    def _op_role(self, role):
Y
yuyang18 已提交
5121 5122 5123
        self._current_role = role

    @property
5124
    def _op_role_var(self):
Y
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5125
        """
5126
        The auxiliary variables for :code:`_op_role` property.
Y
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5127

5128
        See Also: :code:`Program._op_role`'s documentation for details.
Y
yuyang18 已提交
5129 5130 5131

        Notes: This is a very low-level API. Users should not use it directly.
        """
5132
        return self.__op_role_var
Y
yuyang18 已提交
5133

5134
    @signature_safe_contextmanager
5135 5136 5137 5138 5139
    def _backward_role_guard(self):
        tmp_role = self._current_role

        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Backward
5140 5141 5142 5143
        try:
            yield
        finally:
            self._current_role = tmp_role
5144

S
rename  
sneaxiy 已提交
5145
    @signature_safe_contextmanager
W
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5146
    def _optimized_guard(self, param_and_grads):
Y
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5147 5148 5149 5150 5151 5152 5153
        """
        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:
5154
            param_and_grads(list): The variables (names) to be optimized.
Y
yuyang18 已提交
5155 5156 5157

        Examples:

5158
            >>> import paddle.fluid as fluid
Y
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5159
            >>> p, g = backward(...)
W
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5160
            >>> with program._optimized_guard([p,g]):
Y
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5161 5162
            >>>     p = p - 0.001 * g
        """
X
Xin Pan 已提交
5163
        tmp_role = self._current_role
5164
        tmp_var = self.__op_role_var
X
Xin Pan 已提交
5165

Y
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5166 5167
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.Optimize
5168
        self.__op_role_var = [
5169 5170 5171
            var.name if isinstance(var, Variable) else var
            for var in param_and_grads
        ]
5172 5173 5174 5175 5176
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
Y
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5177

S
rename  
sneaxiy 已提交
5178
    @signature_safe_contextmanager
X
Xin Pan 已提交
5179
    def _lr_schedule_guard(self, is_with_opt=False):
5180 5181 5182 5183 5184 5185 5186
        """
        A with guard to set :code:`LRSched` :code:`OpRole` and
        :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
        set to the target learning rate.

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

X
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5187 5188 5189 5190
        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.
5191 5192 5193

        Examples:

5194
            >>> import paddle.fluid as fluid
5195 5196 5197 5198
            >>> p, g = backward(...)
            >>> with program.lr_schedule_guard():
            >>>     lr = lr * decay
        """
5199 5200

        tmp_role = self._current_role
5201
        tmp_var = self.__op_role_var
5202

5203 5204
        OpRole = core.op_proto_and_checker_maker.OpRole
        self._current_role = OpRole.LRSched
X
Xin Pan 已提交
5205 5206
        if is_with_opt:
            self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
5207
        # TODO(typhoonzero): how to set target learning rate var
5208
        self.__op_role_var = []
5209 5210 5211 5212 5213
        try:
            yield
        finally:
            self.__op_role_var = tmp_var
            self._current_role = tmp_role
5214

5215
    def __str__(self):
Y
yuyang18 已提交
5216 5217 5218 5219 5220 5221 5222 5223 5224
        """
        Get the protobuf debug string of this Program.

        Returns:
            (str): The protobuf debug string.

        Raises:
            ValueError: If any of required fields is not set.
        """
5225 5226 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 5238 5239 5240 5241 5242 5243 5244
        return self._to_readable_code()

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

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

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

        Returns:
            string: The formatted Program string.

        Examples:
            .. code-block:: python

5245 5246
            import paddle
            import paddle.static as static
5247

5248 5249 5250
            paddle.enable_static()

            cur_program = static.Program()
5251 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261
            cur_block = cur_program.current_block()
            new_var = cur_block.create_var(name="X",
                                           shape=[-1, 23, 48],
                                           dtype='float32')
            new_op = cur_block.append_op(type="abs",
                                inputs={"X": [new_var]},
                                outputs={"Out": [new_var]})
            print(cur_program._to_readable_code())
        """
        assert isinstance(
            skip_op_callstack, bool
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        ), "skip_op_callstack parameter's type is error, expect bool, received {}".format(
5263 5264 5265 5266
            type(skip_op_callstack))
        program_str = ""
        for block in self.blocks:
            program_str += block._to_readable_code(skip_op_callstack)
5267
            program_str += '\n'
5268
        return program_str
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    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
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        Args:

            throw_on_error (bool): raise Value error when any of required fields is not set.
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            with_details (bool): True if more details about variables and parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need to print.
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        Returns:
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            str: The debug string describe current Program.
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        Raises:
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            ValueError: If any of required fields is not set and throw_on_error is True.
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5286 5287 5288
        Examples:
            .. code-block:: python

5289 5290 5291 5292
                import paddle
                import paddle.static as static

                paddle.enable_static()
5293

5294 5295 5296
                prog = static.default_main_program()
                x = static.data(name="X", shape=[2,3], dtype="float32")
                pred = static.nn.fc(x, size=3)
5297
                prog_string = prog.to_string(throw_on_error=True, with_details=False)
5298
                prog_string_with_details = prog.to_string(throw_on_error=False, with_details=True)
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                print("program string without detail: {}".format(prog_string))
5300
                print("program string with detail: {}".format(prog_string_with_details))
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        """
5302 5303 5304 5305 5306 5307 5308 5309 5310
        assert isinstance(
            throw_on_error, bool
        ), "The type of throw_on_error parameter is wrong, expected bool, but received {}.".format(
            type(throw_on_error))
        assert isinstance(
            with_details, bool
        ), "The type of with_details parameter is wrong, expected bool, but received {}.".format(
            type(with_details))

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        if with_details:
            res_str = ""
            for block in self.blocks:
                res_str += block.to_string(throw_on_error, with_details)
        else:
            protostr = self.desc.serialize_to_string()
5317 5318
            proto = framework_pb2.ProgramDesc.FromString(
                six.binary_type(protostr))
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            res_str = _debug_string_(proto, throw_on_error)
        return res_str
5321

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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.
        """
5330 5331
        return self.desc

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

5335
    def clone(self, for_test=False):
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        """
5337 5338 5339 5340
        .. note:::
            1. :code:`Program.clone()` method DOES NOT clone :ref:`api_paddle_io_DataLoader` . 
            2. Recommend you to use :code:`clone` before using :code:`Opimizer.minimize` . 
            3. This API has no effect in Dygraph Mode.
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5341

5342
        Create a new Program with forward content of original one when ``for_test=True``.
5343
        Create a new Program as same as the original one when ``for_test=False``.
5344

5345
        Some operators, e.g., :ref:`api_paddle_fluid_layers_batch_norm` , behave differently between
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        training and testing. They have an attribute, :code:`is_test`, to
        control this behaviour. This method will change the :code:`is_test`
        attribute of them to :code:`True` when :code:`for_test=True`.
5349

5350 5351
        * Set for_test to False when you want to clone the program for training.
        * Set for_test to True when you want to clone the program for testing.
5352 5353
          We will prune the backward and optimize part of the program when you
          use :code:`clone` after :code:`Opimizer.minimize`, but we still
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          recommend you to use :code:`clone` before using :code:`Opimizer.minimize`.
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5355

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        For Example:
5357
          ::
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5359 5360 5361 5362 5363 5364
            import paddle
            import paddle.static as static

            paddle.enable_static()

            img = static.data(name='image', shape=[None, 784])
5365
            pred = static.nn.fc(x=img, size=10, actvation='relu')
5366
            loss = paddle.mean(pred)
5367
            # Here we use clone before Momentum
5368 5369
            test_program = static.default_main_program().clone(for_test=True)
            optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
5370
            optimizer.minimize(loss)
5371

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5372
        Args:
5373

5374 5375
            for_test (bool): True if change the :code:`is_test` attribute of operators to :code:`True`
                and prune the backward and optimize part of the program. The default value is :code:`False` .
5376

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        Returns:
5378
            Program: A new Program with forward content of original one when ``for_test=True``.  A new Program as same as the original one when ``for_test=False``
5379

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        Examples:

5383 5384 5385 5386 5387 5388 5389
            .. note::
                The Program's order maybe different after :code:`clone` and
                this will not affect your training or testing progress. In the following
                example we give you an simple method :code:`print_prog(program)` to
                print Program Descs inorder to make sure you have same print result
                after :code:`clone`:

5390 5391 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405
            .. code-block:: python

                import six

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


5406
            1. To clone a test program, the sample code is:
5407 5408 5409
                .. code-block:: python

                    import six
5410 5411 5412 5413 5414 5415
                    import paddle
                    import paddle.static as static
                    import paddle.utils as utils
                    import paddle.nn.functional as F

                    paddle.enable_static()
5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427

                    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))

5428 5429
                    train_program = static.Program()
                    startup_program = static.Program()
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                    # startup_program is used to do some parameter init work,
                    # and main program is used to hold the network
5433 5434 5435
                    with static.program_guard(train_program, startup_program):
                        with utils.unique_name.guard():
                            img = static.data(name='image', shape=[None, 784])
5436
                            hidden = static.nn.fc(x=img, size=200, activation='relu')
5437 5438
                            hidden = F.dropout(hidden, p=0.5)
                            loss = F.cross_entropy(
5439
                                input=static.nn.fc(x=hidden, size=10, activation='softmax'),
5440 5441
                                label=static.data(name='label', shape=[1], dtype='int64'))
                            avg_loss = paddle.mean(loss)
5442
                            test_program = train_program.clone(for_test=True)
5443
                    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

5448
                    # In Paddle we will share weights by using the same Tensor name. In train and test program
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                    # 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.

5453 5454 5455
                    with static.program_guard(train_program, startup_program):
                        with utils.unique_name.guard():
                            sgd = paddle.optimizer.SGD(learning_rate=1e-3)
5456 5457 5458
                            sgd.minimize(avg_loss)


5459
            2. The clone method can be avoid if you create program for training and program for testing individually.
5460 5461 5462
                .. code-block:: python

                    import six
5463 5464 5465 5466 5467 5468
                    import paddle
                    import paddle.static as static
                    import paddle.utils as utils
                    import paddle.nn.functional as F

                    paddle.enable_static()
5469 5470 5471 5472 5473 5474 5475 5476 5477 5478 5479

                    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))
5480

5481
                    def network():
5482
                        img = static.data(name='image', shape=[None, 784])
5483
                        hidden = static.nn.fc(x=img, size=200, activation='relu')
5484 5485
                        hidden = F.dropout(hidden, p=0.5)
                        loss = F.cross_entropy(
5486
                            input=static.nn.fc(x=hidden, size=10, activation='softmax'),
5487 5488
                            label=static.data(name='label', shape=[1], dtype='int64'))
                        avg_loss = paddle.mean(loss)
5489 5490
                        return avg_loss

5491 5492 5493 5494 5495
                    train_program_2 = static.Program()
                    startup_program_2 = static.Program()
                    test_program_2 = static.Program()
                    with static.program_guard(train_program_2, startup_program_2):
                        with utils.unique_name.guard():
5496
                            avg_loss = network()
5497
                            sgd = paddle.optimizer.SGD(learning_rate=1e-3)
5498
                            sgd.minimize(avg_loss)
5499
                    # the test startup program is not used.
5500 5501
                    with static.program_guard(test_program_2, startup_program_2):
                        with utils.unique_name.guard():
5502 5503
                            avg_loss = network()
                    print_prog(test_program_2)
5504

5505
            The two code snippets above will generate and print same programs.
5506
        """
5507

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        # NOTE(zhiqiu): we sync the original program first, since its program may diff with
5509 5510 5511
        # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
        self._sync_with_cpp()

5512
        pruned_origin_block_id_map = None
5513
        if for_test:
5514 5515 5516 5517 5518 5519 5520 5521 5522
            forward_prog = Program()
            forward_prog.desc, pruned_origin_block_id_map = core.prune_backward(
                self.desc)
            forward_prog.blocks = [
                Block(forward_prog, i)
                for i in six.moves.range(forward_prog.desc.num_blocks())
            ]
            forward_prog._sync_with_cpp()
            p = forward_prog._inference_optimize(prune_read_op=False)
5523
        else:
5524
            p = Program()
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            p.current_block_idx = self.current_block_idx
            p._seed = self._seed
5527
            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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5531 5532

            p._current_role = self._current_role
5533
            p.__op_role_var = self.__op_role_var
5534
            p._appending_grad_times = self._appending_grad_times
5535 5536
            if hasattr(self, 'lr_sheduler'):
                p.lr_sheduler = self.lr_sheduler
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5537

T
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5538
            # NOTE(zhiqiu): we sync the cloned program, to update its program by
5539
            # its desc.
W
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5540
            p._sync_with_cpp()
5541

W
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5542
        p._copy_param_info_from(self)
5543
        p._copy_data_info_from(self, pruned_origin_block_id_map)
5544
        p._copy_dist_param_info_from(self)
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5545
        return p
5546

5547
    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:
5556
            targets(list|Variable|Operator): A list of variables, operators, or variable names
Y
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5557 5558 5559 5560
                need to be pruned

        Returns:
            Program:  A new, pruned program.
5561
        """
5562
        return self._prune_with_input([], targets)
5563 5564

    def _prune_with_input(self, feeded_var_names, targets):
Y
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5565
        """
5566 5567 5568 5569 5570 5571 5572 5573 5574 5575
        Prune operators and variables which are not needed to generate
        :code:`targets`. Prune operators and variables which are needed 
        to generate feeded_var 

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

        Args:
            feeded_var_names(list|str): A list of variable names from where
                pruning start. If it is set as [], this API works just like _prune()
5576
            targets(list|Variable|Operator): A list of variables, operators, or variable names
5577 5578 5579 5580 5581 5582
                need to be pruned

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

T
tangwei12 已提交
5583
        # NOTE(zhiqiu): we sync the original program first, since its program may diff with
5584 5585 5586
        # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
        self._sync_with_cpp()

5587 5588
        if not isinstance(feeded_var_names, list):
            feeded_var_names = [feeded_var_names]
5589 5590
        if not isinstance(targets, list):
            targets = [targets]
5591 5592 5593

        for var in feeded_var_names:
            if not isinstance(var, six.string_types):
5594 5595 5596
                raise ValueError(
                    "All feeded_var_names of Program._prune_with_input() can only be "
                    "str, but received %s." % type(var))
5597

5598 5599 5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613
        # find out all variables that can be generated or updated with given feed
        generatable_vars = set()

        for idx, op in enumerate(self.global_block().ops):
            runnable_op = True
            for name in op.input_arg_names:
                if not self.global_block().has_var(name):
                    continue
                if self.global_block().var(name).persistable:
                    continue
                if name not in generatable_vars.union(feeded_var_names):
                    runnable_op = False
                    break
            if runnable_op:
                generatable_vars = generatable_vars.union(op.output_arg_names)

5614 5615 5616 5617
        targets_idx = []
        for t in targets:
            if not isinstance(t, Operator):
                if isinstance(t, Variable):
5618 5619 5620
                    name = t.name
                elif isinstance(t, six.string_types):
                    name = str(t)
5621
                else:
5622 5623 5624
                    raise ValueError(
                        "All targets of Program._prune_with_input() can only be "
                        "Variable or Operator, but received %s." % type(t))
5625 5626 5627 5628 5629 5630

                # NOTEZ(zhiqiu): For variable to be fed in fetch_list, there two cases:
                # (1) the variable is leaf, it has no op that generates it;
                # (2) the variable is not leaf, and we need to prune the op that generates it.
                # In both cases, wo can just skip target_op of that it.
                if name in feeded_var_names:
5631 5632 5633
                    # however if the var is also updated by a runnable op, will shall keep it
                    if name not in generatable_vars:
                        continue
5634

5635 5636 5637 5638 5639 5640 5641 5642 5643
                # After transpiler processing, the op that output this
                # variable maybe has been changed, so t.op is not reliable
                # and we need to find the current op that generate this
                # variable here.
                target_op = None
                global_block = self.global_block()
                for idx, op in enumerate(global_block.ops):
                    if name in op.output_arg_names:
                        # NOTE(zhiqiu): Find op that generate target name.
T
tangwei12 已提交
5644
                        # Skip optimize op except for optimize op in targets,
5645 5646 5647 5648 5649
                        # since optimize op generates parameters.
                        if op._is_optimize_op() and op not in targets:
                            continue
                        else:
                            target_op = op
5650

5651
                if target_op is not None:
5652 5653 5654
                    targets_idx.append([target_op.block.idx, target_op.idx])
            else:
                targets_idx.append([t.block.idx, t.idx])
5655

5656
        res = Program()
5657 5658
        res.desc, pruned_origin_block_id_map = core.prune(
            self.desc, set(feeded_var_names), targets_idx)
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5659 5660 5661
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
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5662
        res._sync_with_cpp()
5663 5664 5665 5666 5667

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

5668 5669
        return res

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5670
    def _inference_optimize(self, prune_read_op=True):
Y
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5671
        """
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5672 5673 5674 5675 5676
        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.

5677
        3. change the :code:`is_test`
Y
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5678 5679 5680
        attribute of operators to :code:`True`. All the :code:`Parameter`
        information will be lost.

5681
        Args:
X
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5682 5683
            prune_read_op(bool): remove the read ops that are added by py_reader
                                 for cpp inference library
5684

Y
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5685 5686 5687 5688 5689 5690
        Notes: This API is a very low level API. Use
        :code:`Program.clone(for_test=True)` instead.

        Returns:
            Program: The new program.
        """
5691
        res = Program()
5692
        res.desc = core.ProgramDesc(self.desc)
F
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5693 5694 5695 5696

        # remove all readers and the read_op if exist
        read_op_idx = 0
        root_block = res.desc.block(0)
X
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5697
        if prune_read_op:
5698 5699 5700 5701 5702 5703 5704 5705 5706
            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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5707
                    root_block._remove_var(cpt.to_bytes(var.name()))
F
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5708 5709

        # change all `is_test` attributes to True
M
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5710
        for i in six.moves.range(res.desc.num_blocks()):
5711
            block = res.desc.block(i)
M
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5712
            for j in six.moves.range(block.op_size()):
5713 5714
                op = block.op(j)
                if op.has_attr('is_test'):
W
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5715
                    op._set_attr('is_test', True)
5716 5717 5718
                if op.type() == "batch_norm":
                    # Remove the output ReserveSpace of batch_norm if exists.
                    op.remove_output("ReserveSpace")
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5719 5720 5721
        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
W
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5722
        res._sync_with_cpp()
5723 5724
        return res

5725
    def _remove_training_info(self, clip_extra=True):
5726 5727 5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741 5742 5743 5744 5745 5746 5747 5748 5749
        """
        This method will create a new program and do following adjustments on it:
        1. Remove all variable's `is_parameter` attribute if exist.

        2. Remove all variable's `stop_gradient` attribute if exist.

        Notes: This API is a very low level API.

        Returns:
            Program: The new program.
        """
        res = Program()
        res.desc = core.ProgramDesc(self.desc)

        res.blocks = [
            Block(res, i) for i in six.moves.range(res.desc.num_blocks())
        ]
        res._sync_with_cpp()

        for i in six.moves.range(res.desc.num_blocks()):
            block = res.desc.block(i)
            for var in block.all_vars():
                var.clear_is_parameter()
                var.clear_stop_gradient()
5750 5751 5752 5753 5754 5755 5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769 5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783 5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797 5798 5799 5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811 5812 5813 5814
            if not clip_extra:
                continue
            for op_idx in range(0, block.op_size()):
                op = block.op(op_idx)
                if op.type() not in OpProtoHolder.instance().op_proto_map:
                    continue
                proto = OpProtoHolder.instance().get_op_proto(op.type())
                remove_input_list = []
                for name in op.input_names():
                    find = False
                    for input_proto in proto.inputs:
                        if input_proto.name != name:
                            continue
                        if input_proto.extra:
                            remove_input_list.append(name)
                        find = True
                        break
                    if not find:
                        remove_input_list.append(name)
                for name in remove_input_list:
                    op.remove_input(name)

                remove_output_list = []
                for name in op.output_names():
                    find = False
                    for output_proto in proto.outputs:
                        if output_proto.name != name:
                            continue
                        if output_proto.extra:
                            remove_output_list.append(name)
                        find = True
                        break
                    if not find:
                        remove_output_list.append(name)
                for name in remove_output_list:
                    op.remove_output(name)

                remove_attr_list = []
                op_quant_name = core.op_proto_and_checker_maker.kOpWithQuantAttrName(
                )
                quant = bool(op.attr(op_quant_name)
                             ) if op_quant_name in op.attr_names() else False
                quant_attrs = [
                    op_quant_name, "quantization_type", "skip_quant",
                    "activation_bits", "bit_length", "quantize_weight_bits",
                    "weight_quant_scale"
                ]
                for name in op.attr_names():
                    if quant:
                        if name in quant_attrs:
                            continue
                        if name.endswith("_threshold"):
                            continue
                    find = False
                    for attr_proto in proto.attrs:
                        if attr_proto.name != name:
                            continue
                        if attr_proto.extra:
                            remove_attr_list.append(name)
                        find = True
                        break
                    if not find:
                        remove_attr_list.append(name)
                for name in remove_attr_list:
                    op.remove_attr(name)
5815 5816
        return res

5817 5818
    @staticmethod
    def parse_from_string(binary_str):
Y
yuyang18 已提交
5819
        """
5820 5821 5822
        .. note::
            1. All information about parameters will be lost after serialization; 
            2. This API has no effect in Dygraph mode.
Y
yuyang18 已提交
5823

5824 5825
        Deserialize a Program from  `protobuf <https://en.wikipedia.org/wiki/Protocol_Buffers>`_  binary string.
        This method always use to save and load model
Y
yuyang18 已提交
5826

J
Jiabin Yang 已提交
5827
        Args:
Y
yuyang18 已提交
5828

J
Jiabin Yang 已提交
5829
            binary_str_type (str): the binary prootbuf string.
5830

J
Jiabin Yang 已提交
5831 5832
        Returns:
            Program: A deserialized Program.
5833 5834 5835 5836

        Examples:
            .. code-block:: python

5837 5838 5839 5840
                import paddle
                import paddle.static as static

                paddle.enable_static()
5841

5842 5843 5844 5845
                startup_prog = static.Program()
                main_prog = static.Program()
                with static.program_guard(startup_prog, main_prog):
                    x = static.data(name='X', shape=[1000, 784], dtype='float32')
5846

5847
                    y = static.data(name='Y', shape=[784, 100], dtype='float32')
5848

5849
                    z = paddle.matmul(x=x, y=y)
5850

5851 5852
                    binary_str = static.default_main_program().desc.serialize_to_string()
                    prog_restored = static.default_main_program().parse_from_string(binary_str)
5853

5854
                    print(static.default_main_program())
5855
                    print(prog_restored)
Y
yuyang18 已提交
5856
        """
5857 5858
        p = Program()
        p.desc = core.ProgramDesc(binary_str)
M
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5859
        p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
W
Wu Yi 已提交
5860
        p._sync_with_cpp()
5861
        return p
Y
Yu Yang 已提交
5862

5863
    @staticmethod
5864
    def _construct_from_desc(desc):
5865 5866 5867 5868 5869 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879
        """
        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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5880 5881
    @property
    def random_seed(self):
Y
yuyang18 已提交
5882
        """
J
Jiabin Yang 已提交
5883
        The default random seed for random operators in Program. ``0`` means get
Y
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5884 5885
        the random seed from random device.

5886 5887
        .. note:: 
            It must be set before the operators have been added.
J
Jiabin Yang 已提交
5888 5889 5890

        Returns:
            int64: Random seed in current Program
5891

5892 5893 5894 5895

        Examples:
            .. code-block:: python

5896 5897 5898
                import paddle
                import paddle.static as static
                import paddle.nn.functional as F
5899

5900 5901 5902
                paddle.enable_static()

                prog = static.default_main_program()
5903
                random_seed = prog.random_seed
5904
                x_var = static.data(name="X", shape=[3,3], dtype="float32")
5905 5906 5907
                print(random_seed)
                ## 0
                ## the default random seed is 0
5908

5909
                # Here we need to set random seed before we use paddle.nn.functional.dropout
5910
                prog.random_seed = 1
5911
                z_var = F.dropout(x_var, 0.7)
5912

5913
                print(prog.random_seed)
5914 5915
                ## 1
                ## the random seed is change to 1
Y
yuyang18 已提交
5916
        """
D
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5917 5918
        return self._seed

Q
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5919 5920
    @property
    def num_blocks(self):
Y
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5921
        """
5922 5923
        The number of :ref:`api_guide_Block_en`  in this Program.

5924 5925
        .. note:: 
            This API has no effect in Dygraph mode.
J
Jiabin Yang 已提交
5926 5927 5928

        Returns:
            int(Platform-dependent size): num of :ref:`api_guide_Block_en`  in current Program
5929

5930 5931 5932 5933

        Examples:
            .. code-block:: python

5934 5935 5936 5937
                import paddle
                import paddle.static as static

                paddle.enable_static()
5938

5939
                prog = static.default_main_program()
5940 5941
                num_blocks = prog.num_blocks
                print(num_blocks)
5942

5943 5944
                # print result:
                # 1
Y
yuyang18 已提交
5945
        """
Q
qiaolongfei 已提交
5946 5947
        return self.desc.num_blocks()

D
dzhwinter 已提交
5948 5949 5950
    @random_seed.setter
    def random_seed(self, seed):
        if not isinstance(seed, int):
5951 5952 5953
            raise ValueError(
                "Program.random_seed's input seed must be an integer, but received %s."
                % type(seed))
D
dzhwinter 已提交
5954 5955
        self._seed = seed

Y
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5956
    def __repr__(self):
5957
        return self.__str__()
5958

Y
Yu Yang 已提交
5959
    def global_block(self):
Y
yuyang18 已提交
5960
        """
5961 5962
        .. note::
            This API has no effect in Dygraph mode.
5963 5964 5965

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

J
Jiabin Yang 已提交
5966 5967
        Returns:
            :ref:`api_guide_Block_en`: The first :ref:`api_guide_Block_en`  of this Program.
5968

5969 5970 5971 5972

        Examples:
            .. code-block:: python

5973 5974 5975 5976
                import paddle
                import paddle.static as static

                paddle.enable_static()
5977

5978
                prog = static.default_main_program()
5979 5980
                gb_block = prog.global_block()
                print(gb_block)
5981

Y
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5982
        """
Y
Yu Yang 已提交
5983 5984
        return self.blocks[0]

Q
Qiao Longfei 已提交
5985
    def block(self, index):
Y
yuyang18 已提交
5986
        """
5987 5988
        .. note::
            This API has no effect in Dygraph mode.
Y
yuyang18 已提交
5989

5990 5991
        Get the :code:`index`  :ref:`api_guide_Block_en`  of this Program

J
Jiabin Yang 已提交
5992 5993
        Args:
            index (int) - The index of  :ref:`api_guide_Block_en`  to get
5994

J
Jiabin Yang 已提交
5995 5996
        Returns:
            :ref:`api_guide_Block_en`: The :code:`index` block
5997 5998 5999 6000

        Examples:
            .. code-block:: python

6001 6002 6003 6004
                import paddle
                import paddle.static as static

                paddle.enable_static()
6005

6006
                prog = static.default_main_program()
6007 6008
                block_0 = prog.block(0)
                print(block_0)
Y
yuyang18 已提交
6009
        """
Q
Qiao Longfei 已提交
6010 6011
        return self.blocks[index]

Y
Yu Yang 已提交
6012
    def current_block(self):
Y
yuyang18 已提交
6013
        """
6014 6015
        .. note::
            This API has no effect in Dygraph mode.
6016

J
Jiabin Yang 已提交
6017 6018
        Get the current  :ref:`api_guide_Block_en` . The :code:`current`  :ref:`api_guide_Block_en`
        is the  :ref:`api_guide_Block_en`  to append operators.
6019

J
Jiabin Yang 已提交
6020 6021
        Returns:
             :ref:`api_guide_Block_en`: The :code:`index`  :ref:`api_guide_Block_en`
6022

6023 6024 6025
        Examples:
            .. code-block:: python

6026 6027 6028 6029
                import paddle
                import paddle.static as static

                paddle.enable_static()
6030

6031
                prog = static.default_main_program()
6032 6033
                current_blk = prog.current_block()
                print(current_blk)
Y
yuyang18 已提交
6034
        """
Y
Yu Yang 已提交
6035 6036
        return self.blocks[self.current_block_idx]

W
Wu Yi 已提交
6037
    def _create_block(self, parent_idx=None):
Y
yuyang18 已提交
6038 6039 6040 6041 6042
        """
        Create a new block with the :code:`parent_idx` and change the current block
        to new block.

        Args:
J
Jiabin Yang 已提交
6043

Y
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6044 6045 6046 6047 6048
            parent_idx(int): The parent block index.

        Returns:
            Block: The new block.
        """
Y
Yu Yang 已提交
6049
        new_block_idx = len(self.blocks)
F
update  
fengjiayi 已提交
6050 6051 6052
        parent = self.current_block() if parent_idx is None else self.block(
            parent_idx)
        self.desc.append_block(parent.desc)
Y
Yu Yang 已提交
6053 6054 6055 6056
        self.current_block_idx = new_block_idx
        self.blocks.append(Block(self, self.current_block_idx))
        return self.current_block()

W
Wu Yi 已提交
6057
    def _rollback(self):
Y
yuyang18 已提交
6058 6059 6060 6061 6062
        """
        Exit a code block, i.e., roll back to the parent block.
        Returns:
            None
        """
Y
Yu Yang 已提交
6063 6064
        self.current_block_idx = self.current_block().parent_idx

W
Wu Yi 已提交
6065
    def _sync_with_cpp(self):
Y
yuyang18 已提交
6066 6067 6068 6069 6070 6071 6072 6073 6074 6075
        """
        Synchronize Python instance to its binding C++ object instance.
        If the program is modified in C++ space, this method should be invoked.

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

        Returns:
            None
        """
Q
Qiao Longfei 已提交
6076 6077 6078
        for block_idx in range(len(self.blocks), self.desc.num_blocks()):
            self.blocks.append(Block(self, block_idx))
        for block in self.blocks:
W
Wu Yi 已提交
6079
            block._sync_with_cpp()
Q
Qiao Longfei 已提交
6080

W
Wu Yi 已提交
6081
    def _copy_param_info_from(self, other):
6082
        """
6083
        Copy the information of parameters from other program.
D
dzhwinter 已提交
6084

Y
yuyang18 已提交
6085 6086 6087
        Notes: This is a very low level API. Users should not invoke it
        directly.

6088 6089 6090 6091 6092 6093 6094
        Args:
            other(Program): Other program

        Returns:
            None
        """
        if not isinstance(other, Program):
6095 6096 6097
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
6098

W
Wu Yi 已提交
6099
        self.global_block()._copy_param_info_from(other.global_block())
6100

6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111
    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):
6112 6113 6114
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
6115 6116
        self._is_distributed = other._is_distributed
        self._is_chief = other._is_chief
6117
        self._parameters_on_pservers = other._parameters_on_pservers
6118
        self._endpoints = other._endpoints
6119
        self._ps_endpoint = other._ps_endpoint
6120 6121
        self._distributed_lookup_table = other._distributed_lookup_table

6122
    def _copy_data_info_from(self, other, pruned_origin_block_id_map=None):
F
fengjiayi 已提交
6123 6124
        """
        Copy the information of data variables from other program.
D
dzhwinter 已提交
6125

Y
yuyang18 已提交
6126 6127 6128
        Notes: This is a very low level API. Users should not invoke it
        directly.

F
fengjiayi 已提交
6129 6130
        Args:
            other(Program): Other program
6131 6132 6133 6134
            pruned_origin_block_id_map(dict{int:int}): A dict which maps the block id in program
            self to the block id in program other. For example, {0:0, 1:1, 2:3} means block 0 in self is 
            cloned from block 0 in other, etc. Default is None, which means default mapped, 
            {0:0, 1:1,..., n:n}.
F
fengjiayi 已提交
6135 6136 6137 6138 6139

        Returns:
            None
        """
        if not isinstance(other, Program):
6140 6141 6142
            raise TypeError(
                "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                % type(other))
F
fengjiayi 已提交
6143

6144 6145 6146 6147 6148
        if not pruned_origin_block_id_map:
            pruned_origin_block_id_map = {
                i: i
                for i in six.moves.range(self.desc.num_blocks())
            }
6149 6150 6151

        # NOTE(zhiqiu): All vars in cloned program exist in original program.
        # The reverse is not true, due to backward pruning.
6152 6153
        for i, block in enumerate(self.blocks):
            other_block = other.blocks[pruned_origin_block_id_map[i]]
6154
            for var in list(block.vars.values()):
6155 6156 6157 6158 6159 6160 6161
                other_var = other_block.var(var.name)
                if other_var.is_data:
                    var.is_data = True
                if other_var.desc.need_check_feed():
                    var.desc.set_need_check_feed(True)
                if other_var.stop_gradient:
                    var.stop_gradient = True
F
fengjiayi 已提交
6162

6163
    def list_vars(self):
Y
yuyang18 已提交
6164
        """
6165
        Get all Tensors from this Program. A iterable object is returned.
Y
yuyang18 已提交
6166

J
Jiabin Yang 已提交
6167
        Returns:
6168
            iterable Tensors: The Generator will yield every Tensor in this program.
6169 6170 6171 6172

        Examples:
            .. code-block:: python

6173 6174
                import paddle
                import paddle.static as static
6175

6176 6177 6178 6179 6180
                paddle.enable_static()

                prog = static.default_main_program()
                img = static.data(name='img', shape=[None, 1,28,28], dtype='float32')
                label = static.data(name='label', shape=[None,1], dtype='int64')
6181 6182
                for var in prog.list_vars():
                    print(var)
T
tangwei12 已提交
6183

6184 6185
                # var img : LOD_TENSOR.shape(-1, 1, 28, 28).dtype(float32).stop_gradient(True)
                # var label : LOD_TENSOR.shape(-1, 1).dtype(int64).stop_gradient(True)
Y
yuyang18 已提交
6186
        """
6187
        for each_block in self.blocks:
6188
            for each_var in list(each_block.vars.values()):
6189 6190
                yield each_var

6191 6192 6193 6194 6195 6196 6197 6198 6199 6200
    def all_parameters(self):
        """
        Get all :ref:`api_guide_parameter_en` from this Program. A list object is returned.

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

        Examples:
            .. code-block:: python

6201 6202 6203 6204
                import paddle
                import paddle.static as static

                paddle.enable_static()
6205

6206 6207
                program = static.default_main_program()
                data = static.data(name='x', shape=[None, 13], dtype='float32')
6208
                hidden = static.nn.fc(x=data, size=10)
6209 6210
                loss = paddle.mean(hidden)
                paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
6211 6212 6213 6214 6215 6216 6217

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

                # Here will print all parameters in current program, in this example,
                # the result is like:
                #
6218 6219
                # persist trainable param fc_0.w_0 : LOD_TENSOR.shape(13, 10).dtype(float32).stop_gradient(False)
                # persist trainable param fc_0.b_0 : LOD_TENSOR.shape(10,).dtype(float32).stop_gradient(False)
6220 6221 6222 6223 6224 6225 6226 6227 6228 6229
                #
                # Here print(param) will print out all the properties of a parameter,
                # including name, type and persistable, you can access to specific
                # property of a parameter, such as param.name, param.type
        """
        parameters = []
        for each_block in self.blocks:
            parameters.extend(each_block.all_parameters())
        return parameters

6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252 6253 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266 6267 6268 6269 6270 6271
    def state_dict(self, mode='all', scope=None):
        """
        Get parameters and persistable buffers of program as a dict. The key is the name of the parameter or the name of the buffer.
        The value is the tensor of this variable in the given scope.

        .. note::
            This function MUST called after run start_up_program

        Args:
            mode(str, optional): Source of the obtained parameters and buffers. 
                    'opt' :  The return value only contains the variable in the optimizer. 
                    'param' : The return value only contains the variable in the network, not the variable in the optimizer.  
                    'all' : The return value contains the variable in the network and optimizer.
                    Default: 'all'
            scope(Scope, optional) : If scope is None, state_dict will be set to global scope 
                obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
                Default: None

        Retruns:
            dict: a dict contains the parameters and persistable buffers.

        Examples:
            .. code-block:: python

                import paddle
                import paddle.static as static

                paddle.enable_static()

                x = static.data(name="x", shape=[10, 10], dtype='float32')
                y = static.nn.fc(x, 10)
                z = static.nn.fc(y, 10)

                place = paddle.CPUPlace()
                exe = static.Executor(place)
                exe.run(static.default_startup_program())
                prog = static.default_main_program()

                path = "./temp/model.pdparams"
                paddle.save(prog.state_dict(), path)
        """
        # The 'framework' is a low-level module, and 'executor'
6272
        # can not be imported at the begainning of this file.
6273 6274 6275 6276
        # Therefore, the above two modules are dynamically imported.
        from .executor import global_scope
        if scope is not None and not isinstance(scope, core._Scope):
            raise TypeError(
6277 6278
                "`scope` should be None or `paddle.static.Scope'` type, but received {}."
                .format(type(scope)))
6279 6280 6281 6282 6283

        if scope is None:
            scope = global_scope()

        if not isinstance(mode, str):
6284 6285 6286
            raise TypeError(
                "Type of `mode` should be string, but received {}.".format(
                    type(mode)))
6287 6288 6289 6290 6291 6292 6293 6294 6295 6296 6297 6298 6299 6300 6301 6302 6303 6304 6305 6306 6307 6308 6309 6310 6311 6312

        def is_parameter(var):
            return isinstance(var, Parameter)

        def is_persistable(var):
            if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
                var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
                var.desc.type() == core.VarDesc.VarType.READER:
                return False
            return var.persistable

        def is_belong_to_optimizer(var):
            if not (isinstance(var, Parameter) or var.desc.need_check_feed()):
                return is_persistable(var)
            return False

        def condition(var):

            if mode == 'param':
                return is_parameter(var)
            elif mode == 'opt':
                return is_belong_to_optimizer(var)
            elif mode == 'all':
                return is_parameter(var) or is_belong_to_optimizer(var)
            else:
                raise ValueError(
6313 6314
                    "`mode` string should be 'param', 'opt' or 'all', but received {}."
                    .format(mode))
6315 6316 6317 6318 6319 6320 6321 6322

        var_list = filter(condition, self.list_vars())

        state_dict = dict()
        for var in var_list:
            var_temp = scope.find_var(var.name)
            if var_temp is None:
                raise ValueError(
6323 6324
                    "Can not find Variable '{}' in the scope. Make sure it is initialized"
                    .format(var.name))
6325 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335 6336 6337 6338 6339 6340 6341 6342 6343 6344 6345 6346 6347 6348 6349 6350 6351 6352 6353 6354 6355 6356 6357 6358 6359 6360 6361 6362 6363 6364 6365 6366 6367 6368 6369 6370 6371 6372 6373 6374 6375 6376 6377 6378 6379 6380 6381 6382 6383 6384 6385 6386 6387 6388 6389 6390 6391 6392 6393
            state_dict[var.name] = var_temp.get_tensor()

        return state_dict

    def set_state_dict(self, state_dict, scope=None):
        """
        Set parameters and persistable buffers in state_dict to program. 
        An exception will throw if shape or dtype of the parameters is not match.
        
        .. note::
            This function MUST called after run start_up_program

        Args:
            state_dict(dict): the dict store parameters and persistable buffers. 
                The key is the name of the parameter or the name of the buffer.
                The value is the tensor of this variable in the given scope.
            scope(Scope, optional) : If scope is None, state_dict will be set to global scope 
                obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
                Default: None
        
        Returns:
            None

        Examples:
            .. code-block:: python

                import paddle
                import paddle.static as static

                paddle.enable_static()

                x = static.data(name="x", shape=[10, 10], dtype='float32')
                y = static.nn.fc(x, 10)
                z = static.nn.fc(y, 10)

                place = paddle.CPUPlace()
                exe = static.Executor(place)
                exe.run(static.default_startup_program())
                prog = static.default_main_program()

                path = "./temp/model.pdparams"
                paddle.save(prog.state_dict(), path)
                state_dict_load = paddle.load(path)
                prog.set_state_dict(state_dict_load)
        """

        if not isinstance(state_dict, dict):
            raise TypeError(
                "Type of `state_dict` should be dict, but received {}.".format(
                    type(state_dict)))

        vars_dict = {var.name: var for var in self.list_vars()}
        condition = True if 'StructuredToParameterName@@' in state_dict else False
        for name, value in state_dict.items():
            if condition:
                if name == "StructuredToParameterName@@":
                    continue
                if name in state_dict['StructuredToParameterName@@']:
                    name = state_dict['StructuredToParameterName@@'][name]
            if name in vars_dict:
                try:
                    vars_dict[name].set_value(value, scope)
                except ValueError as err:
                    warnings.warn(
                        ("Skip loading for '{}'. ".format(name) + str(err)))
                except TypeError as err:
                    warnings.warn(
                        ("Skip loading for '{}'. ".format(name) + str(err)))
            else:
6394 6395 6396
                warnings.warn(
                    ("Skip loading for '{0}'. Because '{0}' not in the program."
                     .format(name)))
6397

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6399
@six.add_metaclass(ParameterMetaClass)
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class Parameter(Variable):
6401
    """
6402
    Parameter is derived from Variable. A parameter is a persistable
6403
    Variable, and will be updated by optimizers after each iteration.
6404
    The training of a neural network is essentially the updating of
6405 6406
    its parameters.

6407
    Relative to a general Variable, a Parameter has several its own
6408 6409
    member variables:

6410 6411 6412 6413 6414 6415 6416 6417 6418 6419
    Args:
        trainable(bool): True if the parameter need to be updated after
            iterations.
        optimize_attr(map): Parameter attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the parameter. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this parameter.
6420 6421
        need_clip (bool): Whether the parameter gradient need to be cliped 
            in optimizer. Default is True.
6422 6423
    """

6424 6425 6426 6427 6428 6429
    def __init__(self,
                 block,
                 shape,
                 dtype,
                 type=core.VarDesc.VarType.LOD_TENSOR,
                 **kwargs):
6430 6431 6432 6433 6434
        if shape is None:
            raise ValueError("The shape of Parameter should not be None")
        if dtype is None:
            raise ValueError("The dtype of Parameter should not be None")

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        if len(shape) == 0:
6436 6437
            raise ValueError(
                "The dimensions of shape for Parameter must be greater than 0")
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        for each in shape:
            if each < 0:
6441 6442 6443
                raise ValueError(
                    "Each dimension of shape for Parameter must be greater than 0, but received %s"
                    % list(shape))
6444

6445 6446 6447 6448 6449 6450 6451
        Variable.__init__(self,
                          block,
                          persistable=True,
                          shape=shape,
                          dtype=dtype,
                          type=type,
                          **kwargs)
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        self.trainable = kwargs.get('trainable', True)

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

6456 6457
        self.regularizer = kwargs.get('regularizer', None)

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        self.do_model_average = kwargs.get('do_model_average', None)
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6460 6461
        self.need_clip = kwargs.get('need_clip', True)

6462 6463
        self.is_distributed = False

6464 6465
        self.is_parameter = True

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    def __str__(self):
6467
        return self._to_readable_code()
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F
update  
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    def to_string(self, throw_on_error, with_details=False):
        """
        To debug string.
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update  
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6473 6474 6475 6476 6477 6478 6479 6480
        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.

6481 6482 6483 6484 6485 6486 6487 6488 6489
        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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        """
6491 6492
        assert isinstance(throw_on_error, bool) and isinstance(
            with_details, bool)
F
update  
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        if with_details:
            res_str = Variable.to_string(self, throw_on_error, True)
            additional_attr = ("trainable", "optimize_attr", "regularizer",
6496
                               "do_model_average", "need_clip")
F
update  
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            for attr_name in additional_attr:
6498 6499
                res_str += "%s: %s\n" % (attr_name,
                                         cpt.to_text(getattr(self, attr_name)))
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6500 6501
        else:
            res_str = Variable.to_string(self, throw_on_error, False)
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        return res_str

    __repr__ = __str__

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6507 6508
class ParamBase(core.VarBase):
    """
6509 6510 6511
    ParamBase is derived from Tensor( Which is the concept in Dygraph Mode). 
    A ParamBase is a persistable Tensor, and will be updated by optimizers 
    after each iteration.
6512 6513 6514
    The training of a neural network is essentially the updating of
    its ParamBase.

6515
    Relative to a general Tensor, a ParamBase has several its own
6516 6517 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527
    member variables:

    Args:
        trainable(bool): True if the ParamBase need to be updated after
            iterations.
        optimize_attr(map): ParamBase attributes related with optimizing.
            Currently, it only contains 'learning_rate'.
            Default: {'learning_rate': 1.0}
        regularizer(WeightDecayRegularizer): The Regularizer which will
            be applied on the ParamBase. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this ParamBase.
6528 6529
        need_clip (bool): Whether the parameter gradient need to be cliped 
            in optimizer. Default is True.
6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 6554
    """

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

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

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

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

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

6555 6556 6557 6558
        super(ParamBase,
              self).__init__(dtype if dtype else core.VarDesc.VarType.FP32,
                             list(shape) if shape else [], name,
                             core.VarDesc.VarType.LOD_TENSOR, True)
6559

6560 6561
        trainable = kwargs.get('trainable', True)
        self.stop_gradient = not trainable
6562 6563 6564 6565 6566 6567 6568

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

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

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

6569 6570
        self.need_clip = kwargs.get('need_clip', True)

6571
        self.is_distributed = kwargs.get('is_distributed', False)
6572
        # self.block = default_main_program().global_block()
6573

6574 6575 6576 6577 6578 6579 6580 6581 6582 6583 6584 6585 6586
    @property
    def trainable(self):
        return not self.stop_gradient

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

6587
    def __str__(self):
6588
        """
6589
        Convert a ParamBase object to a readable string.
6590

6591
        Returns(str): A readable string.
6592 6593 6594 6595

        Examples:
            .. code-block:: python

6596
                import paddle
6597 6598 6599 6600 6601 6602 6603
                linear = paddle.nn.Linear(3, 3)
                print(linear.weight)
                # Parameter containing:
                # Tensor(shape=[3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
                #        [[ 0.48948765,  0.05829060, -0.25524026],
                #         [-0.70368278,  0.52986908, -0.68742192],
                #         [-0.54217887,  0.48439729,  0.34082305]])
6604
        """
6605 6606
        return "Parameter containing:\n{tensor}".format(
            tensor=super(ParamBase, self).__str__())
6607

6608 6609 6610 6611 6612 6613 6614 6615 6616 6617 6618
    def __deepcopy__(self, memo):
        """
        Deep copy parameter, it will always performs Tensor copy.

        Examples:
            .. code-block:: python

                import paddle
                import copy
                linear = paddle.nn.Linear(1, 3)
                linear_copy = copy.deepcopy(linear)
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6620 6621 6622 6623 6624 6625 6626 6627 6628 6629 6630 6631 6632 6633 6634 6635 6636 6637
                print(linear.weight)
                # Parameter containing:
                # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                #     [[-0.30929261, -0.90929240, -1.07851017]])

                print(linear_copy.weight)
                # Parameter containing:
                # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                #     [[-0.30929261, -0.90929240, -1.07851017]])

        """
        state = copy.deepcopy(self.__dict__, memo)
        state["name"] = self.name + unique_name.generate("_deepcopy")
        new_param = ParamBase(self.shape, self.dtype, **state)
        memo[id(self)] = new_param
        new_param.copy_(self, True)
        return new_param

6638 6639 6640 6641
    def _copy_to(self, device, blocking):
        state = copy.deepcopy(self.__dict__)
        new_param = ParamBase(self.shape, self.dtype, **state)
        core.varbase_copy(self, new_param, device, blocking)
6642 6643 6644 6645 6646 6647
        return new_param

    __repr__ = __str__


if hasattr(core, "eager"):
6648
    _core_eager_eagertensor = core.eager.Tensor
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else:
    _core_eager_eagertensor = object


class EagerParamBase(_core_eager_eagertensor):
    """
    EagerParamBase is derived from Tensor( Which is the concept in Eager-Dygraph Mode). 
    A EagerParamBase is a persistable Tensor, and will be updated by optimizers 
    after each iteration.
    The training of a neural network is essentially the updating of
    its EagerParamBase.

    Relative to a general Tensor, a EagerParamBase has several its own
    member variables:

    Args:
        trainable(bool): True if the EagerParamBase need to be updated after
            iterations.
        optimize_attr(map): EagerParamBase 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 EagerParamBase. Default: None
        do_model_average(bool): True if the model average strategy will
            be applied on this EagerParamBase.
        need_clip (bool): Whether the parameter gradient need to be cliped 
            in optimizer. Default is True.
    """

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

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

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

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

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

6701 6702 6703
        if isinstance(shape, core.eager.Tensor):
            shape = shape.numpy()

6704 6705 6706 6707
        super(EagerParamBase,
              self).__init__(dtype if dtype else core.VarDesc.VarType.FP32,
                             list(shape) if shape else [], name,
                             core.VarDesc.VarType.LOD_TENSOR, True)
6708 6709 6710 6711 6712 6713 6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727 6728 6729 6730 6731 6732 6733 6734 6735 6736 6737 6738 6739 6740 6741 6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755 6756 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768 6769 6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780 6781 6782 6783 6784 6785 6786 6787 6788 6789 6790 6791
        self.retain_grads()

        trainable = kwargs.get('trainable', True)
        self.stop_gradient = not trainable

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

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

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

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

        self.is_distributed = kwargs.get('is_distributed', False)
        # self.block = default_main_program().global_block()

    @property
    def trainable(self):
        return not self.stop_gradient

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

    def __str__(self):
        """
        Convert a EagerParamBase object to a readable string.

        Returns(str): A readable string.

        Examples:
            .. code-block:: python

                import paddle
                linear = paddle.nn.Linear(3, 3)
                print(linear.weight)
                # Parameter containing:
                # Tensor(shape=[3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
                #        [[ 0.48948765,  0.05829060, -0.25524026],
                #         [-0.70368278,  0.52986908, -0.68742192],
                #         [-0.54217887,  0.48439729,  0.34082305]])
        """
        return "Parameter containing:\n{tensor}".format(
            tensor=super(EagerParamBase, self).__str__())

    def __deepcopy__(self, memo):
        """
        Deep copy parameter, it will always performs Tensor copy.

        Examples:
            .. code-block:: python

                import paddle
                import copy
                linear = paddle.nn.Linear(1, 3)
                linear_copy = copy.deepcopy(linear)

                print(linear.weight)
                # Parameter containing:
                # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                #     [[-0.30929261, -0.90929240, -1.07851017]])

                print(linear_copy.weight)
                # Parameter containing:
                # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                #     [[-0.30929261, -0.90929240, -1.07851017]])

        """
        state = copy.deepcopy(self.__dict__, memo)
        state["name"] = self.name + unique_name.generate("_deepcopy")
        new_param = EagerParamBase(self.shape, self.dtype, **state)
        memo[id(self)] = new_param
        new_param.copy_(self, True)
        return new_param

    def _copy_to(self, device, blocking):
        state = copy.deepcopy(self.__dict__)
        new_param = EagerParamBase(self.shape, self.dtype, **state)
        core.eager.tensor_copy(self, new_param, device, blocking)
6792 6793
        return new_param

6794 6795 6796
    __repr__ = __str__


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# program is a global instance.
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6798 6799
_main_program_ = Program()
_startup_program_ = Program()
6800
_startup_program_._is_start_up_program_ = True
6801

6802

6803
def default_startup_program():
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6804
    """
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6805 6806
    Get default/global startup program.

6807 6808
    The :code:`paddle.nn` function will append the initialization operators into startup program.
    The :code:`startup_program` will initialize the parameters by the OPs. 
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6810 6811
    This method will return the default or the current startup program. Users can use
    :ref:`api_paddle_fluid_framework_program_guard`  to switch :ref:`api_paddle_fluid_framework_Program` .
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6813 6814
    Returns:
        Program: current default startup program.
6815

6816
    Returns type: 
6817 6818 6819 6820

    Examples:
        .. code-block:: python

6821
            import paddle
6822

6823
            paddle.enable_static()
6824 6825 6826 6827
            x = paddle.static.data(name="x", shape=[-1, 784], dtype='float32')
            out = paddle.static.nn.fc(name="fc", x=x, size=10, activation="relu")
            print("main program is: {}".format(paddle.static.default_main_program()))
            print("start up program is: {}".format(paddle.static.default_startup_program()))
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    """
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6829
    return _startup_program_
6830

6831

6832
def default_main_program():
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6833
    """
6834
    This API can be used to get ``default main program`` which store the 
6835
    descriptions of Ops and tensors.
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6836

6837
    For example ``z = paddle.add(x, y)`` will create a new ``add`` 
6838
    Op and a new ``z`` tensor, and they will be recorded in ``default main program`` . 
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6840 6841
    The ``default main program`` is the default value for ``Program`` parameter in 
    a lot of APIs. For example, the :code:`Executor.run()` will execute the
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6842
    :code:`default_main_program` when the program is not specified.
6843

6844
    If you want to switch the ``default main program``, you can use :ref:`api_paddle_fluid_framework_program_guard` .
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6845

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6846
    Returns:
6847
        Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
6848 6849 6850 6851

    Examples:
        ..  code-block:: python

6852
            import paddle
6853

6854
            paddle.enable_static()
6855
            # Sample Network:
6856 6857 6858
            x = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
            y = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
            out = paddle.add(x, y)
6859

6860 6861 6862
            #print the number of blocks in the program, 1 in this case
            print(paddle.static.default_main_program().num_blocks) # 1
            #print the default_main_program
6863
            print(paddle.static.default_main_program())
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    """
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6865
    return _main_program_
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6866 6867 6868 6869 6870


def switch_main_program(program):
    """
    Switch the main program to a new program.
6871

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6872 6873 6874 6875 6876 6877 6878 6879 6880 6881 6882 6883 6884 6885
    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):
    """
6886
    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


S
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6899
@signature_safe_contextmanager
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6900 6901
def program_guard(main_program, startup_program=None):
    """
6902 6903
    :api_attr: Static Graph

6904 6905 6906
    Change the global main program and startup program with ``with`` statement.
    Layer functions in the Python ``with`` block will append operators and
    Tensors to the new main programs.
6907

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    Args:
6909 6910
        main_program(Program): New main program inside ``with`` statement.
        startup_program(Program, optional): New startup program inside ``with`` 
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            statement. :code:`None` means not changing startup program, 
            default_startup_program is still used.
            Default: None.

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    Examples:
6916
       .. code-block:: python
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6918
          import paddle
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6919

6920 6921 6922 6923 6924
          paddle.enable_static()
          main_program = paddle.static.Program()
          startup_program = paddle.static.Program()
          with paddle.static.program_guard(main_program, startup_program):
              data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
6925
              hidden = paddle.static.nn.fc(x=data, size=10, activation='relu')
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6926 6927 6928

    Notes: The temporary :code:`Program` can be used if the user does not need
    to construct either of startup program or main program.
6929

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6930
    Examples:
6931
       .. code-block:: python
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6932

6933
          import paddle
6934

6935 6936 6937 6938 6939
          paddle.enable_static()
          main_program = paddle.static.Program()
          # does not care about startup program. Just pass a temporary value.
          with paddle.static.program_guard(main_program, paddle.static.Program()):
              data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
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Y
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6941
    """
6942
    from .data_feeder import check_type
6943 6944
    check_type(main_program, 'main_program', Program,
               'paddle.static.program_guard')
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Yu Yang 已提交
6945 6946
    main_program = switch_main_program(main_program)
    if startup_program is not None:
6947
        check_type(startup_program, 'startup_program', Program,
6948
                   'paddle.static.program_guard')
6949 6950
        # Tag the program __is_start_up as True
        startup_program._is_start_up_program_ = True
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        startup_program = switch_startup_program(startup_program)
6952 6953 6954 6955 6956 6957
    try:
        yield
    finally:
        switch_main_program(main_program)
        if startup_program is not None:
            switch_startup_program(startup_program)
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def _get_var(name, program=None):
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    """
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    Get a variable by name from the global block of a program.
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    Args:
        name(str): name of the variable
        program(Program|None): program object.
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        If None, default_global_program() will be used.
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    Returns:
        Variable
    """
    if program is None:
        program = default_main_program()
    assert isinstance(name, str)
6975
    assert isinstance(program, Program)
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    return program.global_block().var(name)
6978 6979


S
rename  
sneaxiy 已提交
6980
@signature_safe_contextmanager
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6981 6982
def _dygraph_guard(tracer):
    global _dygraph_tracer_
6983
    tmp_tracer = _dygraph_tracer_
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6984
    _dygraph_tracer_ = tracer
6985
    core._switch_tracer(tracer)
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6987 6988 6989
    try:
        yield
    finally:
6990 6991
        core._switch_tracer(tmp_tracer)
        _dygraph_tracer_ = tmp_tracer
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S
rename  
sneaxiy 已提交
6994
@signature_safe_contextmanager
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6995
def _dygraph_place_guard(place):
6996 6997 6998
    global _global_expected_place_
    tmp_place = _global_expected_place_
    _global_expected_place_ = place
6999 7000
    _set_dygraph_tracer_expected_place(place)

7001 7002 7003
    try:
        yield
    finally:
7004
        _global_expected_place_ = tmp_place
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        _set_dygraph_tracer_expected_place(_global_expected_place_)
7006 7007


7008 7009 7010 7011 7012 7013 7014 7015 7016 7017
def switch_device(device):
    global _current_device
    pre_device = _current_device
    _current_device = device
    return pre_device


@signature_safe_contextmanager
def device_guard(device=None):
    """
7018 7019 7020
    
    Note:
        The API only supports static mode.
7021 7022 7023 7024

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

    Args:
7025 7026
        device(str|None): Specify the device to use in the context. It should be ``cpu``,
            ``gpu`` or ``gpu:x``, where ``x`` is the index of the GPUs. 
7027 7028 7029 7030 7031 7032 7033
            When it is set to 'cpu' or 'gpu', all OPs created in the context will be
            placed on CPUPlace or CUDAPlace. When 'gpu' is set and the program runs on
            single-card, the device index will be the same as the device on which the
            executor runs. Default: None, OPs in this context will be automatically
            assigned devices.

    Examples:
7034
    
7035
        .. code-block:: python
7036 7037
            
            # required: gpu
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            import paddle
7039

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7040 7041 7042
            paddle.enable_static()
            support_gpu = paddle.is_compiled_with_cuda()
            place = paddle.CPUPlace()
7043
            if support_gpu:
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7044
                place = paddle.CUDAPlace(0)
7045 7046

            # if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0)
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            data1 = paddle.full(shape=[1, 3, 8, 8], fill_value=0.5, dtype='float32')
            data2 = paddle.full(shape=[1, 3, 64], fill_value=0.5, dtype='float32')
            shape = paddle.shape(data2)
7050

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7051
            with paddle.static.device_guard("cpu"):
7052
                # Ops created here will be placed on CPUPlace
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                shape = paddle.slice(shape, axes=[0], starts=[0], ends=[4])
            with paddle.static.device_guard('gpu'):
7055
                # if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace
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7056
                out = paddle.reshape(data1, shape=shape)
7057

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7058 7059
            exe = paddle.static.Executor(place)
            exe.run(paddle.static.default_startup_program())
7060 7061 7062
            result = exe.run(fetch_list=[out])
    """

7063 7064 7065 7066 7067
    index = None
    if device and ':' in device:
        device, index = device.split(':')
        if device == 'cpu':
            raise ValueError("Should not set device id for cpu.")
7068
    if device not in ['cpu', 'gpu', 'npu', '', None]:
7069
        raise ValueError(
7070
            "The Attr(device) should be 'cpu' 'npu' or 'gpu', and it can also be empty string or None "
7071
            "when there is no need to specify device. But received %s" % device)
7072 7073
    if index:
        device = ":".join([device, index])
7074
    pre_device = switch_device(device)
7075 7076 7077 7078
    try:
        yield
    finally:
        switch_device(pre_device)
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guofei 已提交
7079 7080


7081 7082 7083 7084 7085 7086 7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097 7098 7099 7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111
def _switch_cuda_graph_mode(cuda_graph_attr):
    global _current_cuda_graph_mode
    pre_mode = _current_cuda_graph_mode
    _current_cuda_graph_mode = cuda_graph_attr
    return pre_mode


@signature_safe_contextmanager
def _cuda_graph_guard(cuda_graph_attr=None):
    """

    Note:
        The API only supports static mode.

    A context manager that specifies the cuda_graph_mode which indicating the cuda graph capture under static mode.

    Args:
        cuda_graph_attr(str|None): The cuda graph attr with the format of:
                                   cuda_graph_capture_mode;memory_pool_id;cuda_graph_id
    """
    assert not _non_static_mode(
    ), "cuda_graph_guard only works under static mode"
    assert core.is_compiled_with_cuda(
    ), "cuda_graph_guard context can be only used when Paddle is compiled with cuda"
    pre_mode = _switch_cuda_graph_mode(cuda_graph_attr)
    try:
        yield
    finally:
        _switch_cuda_graph_mode(pre_mode)


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guofei 已提交
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def set_flags(flags):
    """
    This function sets the GFlags value in Paddle.
7115
    For FLAGS please refer to :ref:`en_guides_flags_flags`
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    Args:
        flags (dict): A dict contains flags and its value.

    Examples:
            .. code-block:: python

7123 7124
                import paddle
                paddle.set_flags({'FLAGS_eager_delete_tensor_gb': 1.0})
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    """
    if not isinstance(flags, dict):
        raise TypeError('flags in set_flags should be a dict')
    for key, value in flags.items():
7129 7130
        if _global_flags().is_public(key):
            _global_flags()[key] = value
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guofei 已提交
7131 7132 7133 7134 7135 7136 7137 7138
        else:
            raise ValueError(
                "Flag %s cannot set its value through this function." % (key))


def get_flags(flags):
    """
    This function gets the GFlags value in Paddle.
7139
    For FLAGS please refer to :ref:`en_guides_flags_flags`
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7140 7141 7142 7143 7144 7145 7146 7147 7148 7149

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

    Returns:
        flag's value in Paddle.

    Examples:
        .. code-block:: python

7150
            import paddle
G
guofei 已提交
7151 7152

            flags = ['FLAGS_eager_delete_tensor_gb', 'FLAGS_check_nan_inf']
7153
            res = paddle.get_flags(flags)
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7154 7155 7156 7157 7158 7159
            print(res)
            # {'FLAGS_eager_delete_tensor_gb': 0.0, 'FLAGS_check_nan_inf': False}
    """
    flags_value = {}
    if isinstance(flags, (list, tuple)):
        for key in flags:
7160 7161
            if (_global_flags().is_public(key)):
                value = _global_flags()[key]
G
guofei 已提交
7162 7163 7164 7165 7166 7167 7168
                temp = {key: value}
                flags_value.update(temp)
            else:
                raise ValueError(
                    'Flag %s cannot get its value through this function.' %
                    (key))
    elif isinstance(flags, str):
7169 7170
        if (_global_flags().is_public(flags)):
            value = _global_flags()[flags]
G
guofei 已提交
7171 7172 7173 7174 7175 7176 7177 7178
            temp = {flags: value}
            flags_value.update(temp)
        else:
            raise ValueError(
                'Flag %s cannot get its value through this function.' % (flags))
    else:
        raise TypeError('Flags in get_flags should be a list, tuple or string.')
    return flags_value
7179 7180 7181 7182 7183 7184 7185


def _get_paddle_place(place):
    "convert the string to paddle Place"
    if place is None:
        return place
    if isinstance(place, (core.Place, core.XPUPlace, core.CPUPlace,
7186
                          core.CUDAPinnedPlace, core.CUDAPlace, core.NPUPlace,
7187
                          core.IPUPlace, core.MLUPlace, core.CustomPlace)):
7188 7189 7190 7191 7192 7193 7194 7195 7196
        return place

    if not isinstance(place, str):
        raise ValueError(
            "place only support string which is 'Place' and so on.")

    place = place.lower()
    if (place == "cpu"):
        return core.CPUPlace()
7197

7198 7199 7200
    if (place == "device"):
        return core.Place()

7201
    # GPU
7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212 7213 7214 7215 7216
    avaliable_gpu_place = re.match(r'gpu:\d+', place)
    if place == "gpu_pinned" or place == "gpu" or avaliable_gpu_place:
        if not core.is_compiled_with_cuda():
            raise ValueError(
                "The device should not be {}, since PaddlePaddle is " \
                "not compiled with CUDA".format(avaliable_gpu_place))
        if place == "gpu_pinned":
            return core.CUDAPinnedPlace()
        elif place == "gpu":
            return core.CUDAPlace(0)
        else:
            place_info_list = place.split(':', 1)
            device_id = place_info_list[1]
            device_id = int(device_id)
            return core.CUDAPlace(device_id)
7217 7218

    # XPU
7219 7220 7221 7222 7223 7224 7225 7226 7227 7228
    avaliable_xpu_place = re.match(r'xpu:\d+', place)
    if avaliable_xpu_place:
        if not core.is_compiled_with_xpu():
            raise ValueError(
                "The device should not be {}, since PaddlePaddle is " \
                "not compiled with XPU".format(avaliable_xpu_place))
        place_info_list = place.split(':', 1)
        device_id = place_info_list[1]
        device_id = int(device_id)
        return core.XPUPlace(device_id)
7229 7230 7231 7232 7233 7234 7235 7236 7237 7238 7239 7240 7241

    # NPU
    avaliable_npu_place = re.match(r'npu:\d+', place)
    if avaliable_npu_place:
        if not core.is_compiled_with_npu():
            raise ValueError(
                "The device should not be {}, since PaddlePaddle is " \
                "not compiled with NPU".format(avaliable_npu_place))
        place_info_list = place.split(':', 1)
        device_id = place_info_list[1]
        device_id = int(device_id)
        return core.NPUPlace(device_id)

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jianghaicheng 已提交
7242 7243 7244 7245 7246 7247 7248 7249 7250 7251 7252 7253
    # IPU
    avaliable_ipu_place = re.match(r'ipu:\d+', place)
    if avaliable_ipu_place:
        if not core.is_compiled_with_ipu():
            raise ValueError(
                "The device should not be {}, since PaddlePaddle is " \
                "not compiled with IPU".format(avaliable_ipu_place))
        place_info_list = place.split(':', 1)
        device_id = place_info_list[1]
        device_id = int(device_id)
        return core.IPUPlace(device_id)

7254 7255 7256 7257 7258 7259 7260 7261 7262 7263 7264 7265
    # MLU
    avaliable_mlu_place = re.match(r'mlu:\d+', place)
    if avaliable_mlu_place:
        if not core.is_compiled_with_mlu():
            raise ValueError(
                "The device should not be {}, since PaddlePaddle is " \
                "not compiled with MLU".format(avaliable_mlu_place))
        place_info_list = place.split(':', 1)
        device_id = place_info_list[1]
        device_id = int(device_id)
        return core.MLUPlace(device_id)

7266
    raise ValueError(
7267 7268
        "Paddle supports CPUPlace, CUDAPlace,CUDAPinnedPlace, XPUPlace, IPUPlace, MLUPlace and NPUPlace, but received {}."
        .format(place))
7269 7270 7271 7272 7273 7274 7275 7276 7277 7278 7279 7280 7281


def _get_paddle_place_list(places):

    if not isinstance(places, (list, tuple)):
        raise TypeError("places must to be List or Tuple")

    ret = []
    for p in places:
        p = _get_paddle_place(p)
        ret.append(p)

    return ret