control_flow.py

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

from __future__ import print_function
from ..wrapped_decorator import signature_safe_contextmanager

from .layer_function_generator import autodoc, templatedoc
from .tensor import assign, fill_constant
from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from .nn import logical_and, logical_not, logical_or
import numpy
import warnings
import six
from functools import reduce

__all__ = [
    'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
    'less_equal', 'greater_than', 'greater_equal', 'equal', 'not_equal',
    'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
    'reorder_lod_tensor_by_rank', 'Print', 'is_empty'
]


def split_lod_tensor(input, mask, level=0):
    """
    This function takes in an input that contains the complete lod information,
    and takes in a mask which is used to mask certain parts of the input.
    The output is the true branch and the false branch with the mask applied to
    the input at a certain level in the tensor. Mainly used in IfElse to split
    data into two parts.

    Args:
        input(tuple|list|None): The input tensor that contains complete
                                lod information needed to construct the output.
        mask(list): A bool column vector which masks the input.
        level(int): The specific lod level to split.

    Returns:
        tuple(Variable, Variable):
        The true branch of tensor as per the mask applied to input.

        The false branch of tensor as per the mask applied to input.

    Examples:
        .. code-block:: python

          x = fluid.layers.data(name='x', shape=[1])
          x.persistable = True

          y = fluid.layers.data(name='y', shape=[1])
          y.persistable = True

          out_true, out_false = fluid.layers.split_lod_tensor(
                input=x, mask=y, level=level)

    """
    helper = LayerHelper('split_lod_tensor', **locals())
    out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
    out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type='split_lod_tensor',
        inputs={
            'X': input,
            'Mask': mask,
        },
        outputs={'OutTrue': out_true,
                 'OutFalse': out_false},
        attrs={'level': level})
    return out_true, out_false


def merge_lod_tensor(in_true, in_false, x, mask, level=0):
    """
    **merge_lod_tensor**

    This function takes in an input :math:`x`, the True branch, the False
    branch and a binary :math:`mask`. Using this information, this function
    merges the True and False branches of the tensor into a single tensor as
    output at a certain lod level indicated by :math:`level`. Used in IfElse
    to merge the output if True block and False Block.

    Args:
        in_true(tuple|list|None): The True branch to be merged.
        in_false(tuple|list|None): The False branch to be merged.
        x(tuple|list|None): The input tensor that contains complete
                            lod information needed to construct the output.
        mask(list): A bool column vector which masks the input.
        level(int): The specific lod level to merge.

    Returns:
        Variable: The merged output tensor.

    Examples:
        .. code-block:: python

          x = layers.data(
                      name='x', shape=[1], dtype='float32', stop_gradient=False)
          y = layers.data(
                name='y', shape=[1], dtype='bool', stop_gradient=False)

          level = 0

          out_true, out_false = layers.split_lod_tensor(
                input=x, mask=y, level=level)
          out = layers.merge_lod_tensor(
                in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
    """
    helper = LayerHelper('merge_lod_tensor', **locals())
    out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
    helper.append_op(
        type='merge_lod_tensor',
        inputs={'X': x,
                'Mask': mask,
                'InTrue': in_true,
                'InFalse': in_false},
        outputs={'Out': out},
        attrs={'level': level})
    return out


def Print(input,
          first_n=-1,
          message=None,
          summarize=-1,
          print_tensor_name=True,
          print_tensor_type=True,
          print_tensor_shape=True,
          print_tensor_lod=True,
          print_phase='both'):
    '''
    **Print operator**

    This creates a print op that will print when a tensor is accessed.

    Wraps the tensor passed in so that whenever that a tensor is accessed,
    the message `message` is printed, along with the current value of the
    tensor `t`.

    Args:
        input (Variable): A Tensor to print.
        summarize (int): Print this number of elements in the tensor, will print
                all if left is negative.
        message (str): A string message to print as a prefix.
        first_n (int): Only log `first_n` number of times.
        print_tensor_name (bool): Print the tensor name.
        print_tensor_type (bool): Print the tensor type.
        print_tensor_shape (bool): Print the tensor shape.
        print_tensor_lod (bool): Print the tensor lod.
        print_phase (str): Which phase to displace, including 'forward',
                'backward' and 'both'. If set to 'backward' or 'both', will
                print the gradients of input tensor.

    Returns:
        Variable: Output tensor, same data with input tensor.


    Examples:

        .. code-block:: python

           value = some_layer(...)
           Print(value, summarize=10,
               message="The content of some_layer: ")
    '''
    helper = LayerHelper('print', **locals())
    helper.append_op(
        type='print',
        inputs={'In': input},
        attrs={
            'first_n': first_n,
            'summarize': summarize,
            'message': message or "",
            'print_tensor_name': print_tensor_name,
            'print_tensor_type': print_tensor_type,
            'print_tensor_shape': print_tensor_shape,
            'print_tensor_lod': print_tensor_lod,
            'print_phase': print_phase.upper()
        })
    return input


class BlockGuard(object):
    """
    BlockGuard class.

    BlockGuard class is used to create a sub-block in a program by
    using the Python `with` keyword.
    """

    def __init__(self, main_program):
        if not isinstance(main_program, Program):
            raise TypeError("BlockGuard takes a program")
        self.main_program = main_program

    def __enter__(self):
        self.main_program._create_block()

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.main_program._rollback()
        if exc_type is not None:
            return False  # re-raise exception
        return True


class BlockGuardWithCompletion(BlockGuard):
    """
    BlockGuardWithCompletion class.

    BlockGuardWithCompletion class is used to create an op with a block in a program.
    """

    def __init__(self, rnn):
        if not isinstance(rnn, StaticRNN):
            raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
        super(BlockGuardWithCompletion, self).__init__(rnn.helper.main_program)
        self.rnn = rnn

    def __enter__(self):
        self.rnn.status = StaticRNN.IN_RNN_BLOCK
        return super(BlockGuardWithCompletion, self).__enter__()

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is not None:
            return False
        self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
        self.rnn._complete_op()
        return super(BlockGuardWithCompletion, self).__exit__(exc_type, exc_val,
                                                              exc_tb)


class StaticRNNMemoryLink(object):
    """
    StaticRNNMemoryLink class.

    StaticRNNMemoryLink class is used to create a link between two
    memory cells of a StaticRNN.


    NOTE: This is a internal data structure of a very low-level API.
    Please use StaticRNN instead.

    Args:
        init(Variable): the initial variable for Memory.
        pre_mem(Variable): the memory variable in previous time step.
        mem(Variable): the memory variable in current time step.
    """

    def __init__(self, init, pre_mem, mem=None):
        self.init = init
        self.pre_mem = pre_mem
        self.mem = mem


class StaticRNN(object):
    """
    StaticRNN class.

    The StaticRNN can process a batch of sequence data. The length of each
    sample sequence must be equal. The StaticRNN will have its own parameters
    like inputs, outputs, memories. **Note that the first dimension of inputs
    represents sequence length, and all the sequence length of inputs must be
    the same. And the meaning of each axis of input and output are the same.**

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle.fluid.layers as layers

            vocab_size, hidden_size=10000, 200
            x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
            x_emb = layers.embedding(
                input=x,
                size=[vocab_size, hidden_size],
                dtype='float32',
                is_sparse=False)
            x_emb = layers.transpose(x_emb, perm=[1, 0, 2])

            rnn = fluid.layers.StaticRNN()
            with rnn.step():
                word = rnn.step_input(x_emb)
                prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
                hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
                rnn.update_memory(prev, hidden)  # set prev to hidden
                rnn.step_output(hidden)

            result = rnn()

    The StaticRNN will unfold sequence into time steps. Users need to define
    how to process each time step during the :code:`with` step.

    The :code:`memory` is used as a staging data cross time step. The initial
    value of memory can be a variable that is filled with a constant value or
    a specified variable.

    The StaticRNN can mark multiple variables as its output. Use `rnn()` to
    get the output sequence.
    """
    BEFORE_RNN_BLOCK = 0
    IN_RNN_BLOCK = 1
    AFTER_RNN_BLOCK = 2

    def __init__(self, name=None):
        self.helper = LayerHelper("static_rnn", name=name)
        self.memories = {}  # memory map, from pre_mem.name --> MemoryLink
        self.inputs = []  # input variable list in current block
        self.outputs = []  # output variable list in parent block
        self.status = StaticRNN.BEFORE_RNN_BLOCK  # status flag.
        # sequence length, since it is a static RNN, sequence length are fixed.
        self.seq_len = None

    def step(self):
        """
        The block for user to define operators in RNN.
        """
        return BlockGuardWithCompletion(self)

    def _assert_in_rnn_block_(self, method):
        if self.status != StaticRNN.IN_RNN_BLOCK:
            raise ValueError("You must invoke {0} in rnn block".format(method))

    def memory(self,
               init=None,
               shape=None,
               batch_ref=None,
               init_value=0.0,
               init_batch_dim_idx=0,
               ref_batch_dim_idx=1):
        """
        Create a memory variable for static rnn.

        If the :code:`init` is not None, :code:`memory` will be initialized by
        this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
        must be set, and this function will initialize a :code:`init` Variable.

        Args:
            init(Variable|None): The initialized variable. If it is not set,
                :code:`shape` and :code:`batch_ref` must be provided.
                Default: None.
            shape(list|tuple): The shape of the boot memory. NOTE the shape
                does not contain batch_size. Default: None.
            batch_ref(Variable|None): The batch size reference Variable.
                Default: None.
            init_value(float): the init value of boot memory. Default: 0.0.
            init_batch_dim_idx(int): the batch_size axis of the
                :code:`init` Variable. Default: 0.
            ref_batch_dim_idx(int): the batch_size axis of the
                :code:`batch_ref` Variable. Default: 1.

        Returns:
            The memory variable.
        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import paddle.fluid.layers as layers

                vocab_size, hidden_size=10000, 200
                x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
                x_emb = layers.embedding(
                    input=x,
                    size=[vocab_size, hidden_size],
                    dtype='float32',
                    is_sparse=False)
                x_emb = layers.transpose(x_emb, perm=[1, 0, 2])

                rnn = fluid.layers.StaticRNN()
                with rnn.step():
                    word = rnn.step_input(x_emb)
                    prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
                    hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
                    rnn.update_memory(prev, hidden)
        """
        self._assert_in_rnn_block_('memory')
        if init is None:
            if shape is None or batch_ref is None:
                raise ValueError(
                    "if init is None, memory at least need shape and batch_ref")
            parent_block = self._parent_block()
            var_name = unique_name.generate("@".join(
                [self.helper.name, "memory_boot"]))
            boot_var = parent_block.create_var(
                name=var_name,
                shape=shape,
                dtype=batch_ref.dtype,
                persistable=False)

            parent_block.append_op(
                type="fill_constant_batch_size_like",
                inputs={'Input': [batch_ref]},
                outputs={'Out': [boot_var]},
                attrs={
                    'value': init_value,
                    'shape': boot_var.shape,
                    'dtype': boot_var.dtype,
                    'input_dim_idx': ref_batch_dim_idx,
                    'output_dim_idx': init_batch_dim_idx
                })

            return self.memory(init=boot_var)
        else:
            pre_mem = self.helper.create_variable(
                name=unique_name.generate("@".join([self.helper.name, "mem"])),
                dtype=init.dtype,
                shape=init.shape)
            self.memories[pre_mem.name] = StaticRNNMemoryLink(
                init=init, pre_mem=pre_mem)
            return pre_mem

    def step_input(self, x):
        """
        Mark a sequence as a StaticRNN input.

        Args:
            x(Variable): The input sequence, the shape of x
                should be [seq_len, ...].

        Returns:
            The current time step in the input sequence.
        """
        self._assert_in_rnn_block_('step_input')
        if not isinstance(x, Variable):
            raise TypeError("step input takes a Variable")
        if self.seq_len is None:
            self.seq_len = x.shape[0]
        elif self.seq_len != x.shape[0]:
            raise ValueError("Static RNN only take fix seq_len input")

        ipt = self.helper.create_variable(
            name=x.name, dtype=x.dtype, shape=list(x.shape[1:]), type=x.type)
        self.inputs.append(ipt)
        return ipt

    def step_output(self, o):
        """
        Mark a sequence as a StaticRNN output.

        Args:
            o(Variable): The output sequence.

        Returns:
            None.
        """
        self._assert_in_rnn_block_('step_output')
        if not isinstance(o, Variable):
            raise TypeError("step output takes a Variable")

        tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
        self.helper.append_op(
            type='rnn_memory_helper',
            inputs={'X': [o]},
            outputs={'Out': tmp_o},
            attrs={'dtype': o.dtype})

        out_var = self._parent_block().create_var(
            name=tmp_o.name,
            shape=[self.seq_len] + list(tmp_o.shape),
            dtype=tmp_o.dtype)

        self.outputs.append(out_var)

    def output(self, *outputs):
        """
        Mark the StaticRNN output variables.

        Args:
            outputs: The output Variables.

        Returns:
            None
        """
        for each in outputs:
            self.step_output(each)

    def update_memory(self, mem, var):
        """
        Update the memory from ex_mem to new_mem. NOTE that the shape and data
        type of :code:`ex_mem` and :code:`new_mem` must be same.

        Args:
            mem(Variable): the memory variable.
            var(Variable): the plain variable generated in RNN block.

        Returns:
            None
        """
        if not isinstance(mem, Variable) or not isinstance(var, Variable):
            raise TypeError("update memory should take variables")
        self.memories[mem.name].mem = var

    def _parent_block(self):
        prog = self.helper.main_program
        parent_idx = prog.current_block().parent_idx
        assert parent_idx >= 0
        parent_block = prog.block(parent_idx)
        return parent_block

    def __call__(self, *args, **kwargs):
        if self.status != StaticRNN.AFTER_RNN_BLOCK:
            raise ValueError("RNN output can only be retrieved after rnn block")
        if len(self.outputs) == 0:
            raise ValueError("RNN has no output")
        elif len(self.outputs) == 1:
            return self.outputs[0]
        else:
            return self.outputs

    def _complete_op(self):
        main_program = self.helper.main_program
        rnn_block = main_program.current_block()
        parent_block = self._parent_block()

        local_inputs = set()

        for op in rnn_block.ops:
            assert isinstance(op, Operator)
            for oname in op.output_names:
                for out_var_name in op.output(oname):
                    local_inputs.add(out_var_name)

        for var in self.inputs:
            local_inputs.add(var.name)
        for m in self.memories:
            local_inputs.add(m)

        # NOTE(zcd): the params have two categories of variables.
        #   - the variables that are the out of StaticRnn.
        #   - the variables that are the parameters of some layers, for example, conv2d.
        params = list()
        for op in rnn_block.ops:
            assert isinstance(op, Operator)
            for iname in op.input_names:
                for in_var_name in op.input(iname):
                    if in_var_name not in local_inputs:
                        params.append(in_var_name)

        parameters = [parent_block.var(name) for name in params]

        step_scope = parent_block.create_var(
            type=core.VarDesc.VarType.STEP_SCOPES)

        inlinks = [parent_block.var(i.name) for i in self.inputs]
        outlinks = self.outputs

        # NOTE(zcd): the states maybe empty in some case.
        boot_memories = []
        pre_memories = []
        memories = []
        for _, mem in six.iteritems(self.memories):
            boot_memories.append(mem.init)
            pre_memories.append(mem.pre_mem.name)
            assert mem.mem is not None, "%s should be updated in every step." % (
                mem.init.name)
            mem_var = rnn_block.var(mem.mem.name)
            assert isinstance(mem_var, Variable)
            new_mem = self.helper.create_variable_for_type_inference(
                dtype=mem_var.dtype)
            rnn_block.append_op(
                type='rnn_memory_helper',
                inputs={'X': [mem_var]},
                outputs={'Out': [new_mem]},
                attrs={'dtype': mem_var.dtype})

            memories.append(new_mem.name)

        parent_block.append_op(
            type='recurrent',
            inputs={
                'inputs': inlinks,
                'initial_states': boot_memories,
                'parameters': parameters
            },
            outputs={'outputs': outlinks,
                     'step_scopes': [step_scope]},
            attrs={
                'has_states': len(pre_memories) > 0,
                'ex_states': pre_memories,
                'states': memories,
                'sub_block': rnn_block
            })


class WhileGuard(BlockGuard):
    def __init__(self, while_op):
        if not isinstance(while_op, While):
            raise TypeError("WhileGuard takes a while op")
        super(WhileGuard, self).__init__(while_op.helper.main_program)
        self.while_op = while_op

    def __enter__(self):
        self.while_op.status = While.IN_WHILE_BLOCK
        return super(WhileGuard, self).__enter__()

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is not None:
            return False
        self.while_op.status = While.AFTER_WHILE_BLOCK
        self.while_op._complete()
        return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb)


class While(object):
    """
    while loop control flow.

    Args:
        cond(Variable): condition used to compare.
        is_test(bool): A flag indicating whether execution is in test phase.
        name(str): The name of this layer.

    Examples:
          .. code-block:: python

            d0 = layers.data("d0", shape=[10], dtype='float32')
            data_array = layers.array_write(x=d0, i=i)
            array_len = layers.fill_constant(shape=[1],dtype='int64', value=3)

            cond = layers.less_than(x=i, y=array_len)
            while_op = layers.While(cond=cond)
            with while_op.block():
                d = layers.array_read(array=data_array, i=i)
                i = layers.increment(x=i, in_place=True)
                layers.array_write(result, i=i, array=d)
                layers.less_than(x=i, y=array_len, cond=cond)
    """

    BEFORE_WHILE_BLOCK = 0
    IN_WHILE_BLOCK = 1
    AFTER_WHILE_BLOCK = 2

    def __init__(self, cond, is_test=False, name=None):
        self.helper = LayerHelper("while", name=name)
        self.status = While.BEFORE_WHILE_BLOCK
        if not isinstance(cond, Variable):
            raise TypeError("condition should be a variable")
        assert isinstance(cond, Variable)
        if cond.dtype != core.VarDesc.VarType.BOOL:
            raise TypeError("condition should be a bool variable")
        if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
            raise TypeError("condition should be a bool scalar")
        self.cond_var = cond
        self.is_test = is_test

    def block(self):
        return WhileGuard(self)

    def _complete(self):
        main_program = self.helper.main_program
        while_block = main_program.current_block()
        parent_block = main_program.block(main_program.current_block()
                                          .parent_idx)

        inner_outputs = {self.cond_var.name}
        x_name_list = set()
        for op in while_block.ops:
            for iname in op.input_names:
                for in_var_name in op.input(iname):
                    if in_var_name not in inner_outputs:
                        x_name_list.add(in_var_name)

            for oname in op.output_names:
                for out_var_name in op.output(oname):
                    inner_outputs.add(out_var_name)

        out_vars = []
        for inner_out_name in inner_outputs:
            inner_var = parent_block._find_var_recursive(inner_out_name)
            if inner_var:
                out_vars.append(inner_var)

        step_scope = parent_block.create_var(
            type=core.VarDesc.VarType.STEP_SCOPES)

        parent_block.append_op(
            type='while',
            inputs={
                'X': [
                    parent_block._var_recursive(x_name)
                    for x_name in x_name_list
                ],
                'Condition': [self.cond_var]
            },
            outputs={'Out': out_vars,
                     'StepScopes': [step_scope]},
            attrs={'sub_block': while_block,
                   "is_test": self.is_test})


def lod_rank_table(x, level=0):
    """
    LoD Rank Table Operator. Given an input variable **x** and a level number
    of LoD, this layer creates a LodRankTable object. A LoDRankTable object
    contains a list of bi-element tuples. Each tuple consists of an index and
    a length, both of which are int type. Refering to specified level of LoD,
    the index is the sequence index number and the length representes the
    sequence length. Please note that the list is ranked in descending order by
    the length. The following is an example:

        .. code-block:: text

            x is a LoDTensor:
                x.lod = [[2,                1],
                         [5,             1, 1]]
                x.data = [a, b, c, d, e, f, g]

            1. set level to 0:
                Create lod rank table:
                    lod_rank_table_obj = lod_rank_table(x, level=0)

                Get:
                    lod_rank_table_obj.items() = [(0, 2), (1, 1)]

            2. set level to 1:
                Create lod rank table:
                    lod_rank_table_obj = lod_rank_table(x, level=1)

                Get:
                    lod_rank_table_obj.items() = [(0, 5), (1, 1), (2, 1)]

    Args:
        x (Variable): Input variable, a LoDTensor based which to create the lod
            rank table.
        level (int): Specify the LoD level, on which to create the lod rank
            table.

    Returns:
        Variable: The created LoDRankTable object.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[10],
                                  dtype='float32', lod_level=1)
            out = layers.lod_rank_table(x=x, level=0)
    """
    helper = LayerHelper("lod_rank_table", **locals())
    table = helper.create_variable(
        type=core.VarDesc.VarType.LOD_RANK_TABLE,
        name=unique_name.generate("lod_rank_table"))
    helper.append_op(
        type='lod_rank_table',
        inputs={'X': x},
        outputs={'Out': table},
        attrs={'level': level})
    return table


@templatedoc()
def max_sequence_len(rank_table):
    """
    ${comment}

    >>> import paddle.fluid as fluid
    >>> x = fluid.layers.data(name='x', shape=[10], dtype='float32',
    >>>                       lod_level=1)
    >>> rank_table = layers.lod_rank_table(x=x, level=0)
    >>> max_seq_len = layers.max_sequence_len(rank_table)

    Args:
        rank_table(${rank_table_type}): ${rank_table_comment}.

    Returns:
        ${out_comment}.
    """
    helper = LayerHelper("max_seqence_len", **locals())
    res = helper.create_variable_for_type_inference(dtype="int64")
    helper.append_op(
        type="max_sequence_len",
        inputs={"RankTable": rank_table},
        outputs={"Out": res})
    return res


def lod_tensor_to_array(x, table):
    """
    Convert a LoDTensor to a LoDTensorArray.

    This function split a LoDTesnor to a LoDTensorArray according to its LoD
    information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
    PaddlePaddle. The generated LoDTensorArray of this function can be further read
    or written by `read_from_array()` and `write_to_array()` operators. However,
    this function is generally an internal component of PaddlePaddle `DynamicRNN`.
    Users should not use it directly.

    Args:
        x (Variable|list): The LoDTensor to be converted to a LoDTensorArray.
        table (ParamAttr|list): The variable that stores the level of lod
                                which is ordered by sequence length in
                                descending order. It is generally generated
                                by `layers.lod_rank_table()` API.

    Returns:
        Variable: The LoDTensorArray that has been converted from the input tensor.

    Examples:
        .. code-block:: python

          x = fluid.layers.data(name='x', shape=[10])
          table = fluid.layers.lod_rank_table(x, level=0)
          array = fluid.layers.lod_tensor_to_array(x, table)
    """
    helper = LayerHelper("lod_tensor_to_array", **locals())
    array = helper.create_variable(
        name=unique_name.generate("lod_tensor_to_array"),
        type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
        dtype=x.dtype)
    helper.append_op(
        type='lod_tensor_to_array',
        inputs={'X': x,
                'RankTable': table},
        outputs={'Out': array})
    return array


def array_to_lod_tensor(x, table):
    """Convert a LoD_Tensor_Aarry to an LoDTensor.

    Args:
        x (Variable|list): The lod tensor array to be converted to a tensor.
        table (ParamAttr|list): The variable that stores the level of lod
                                which is ordered by sequence length in
                                descending order.

    Returns:
        Variable: The variable of type tensor that has been converted
                  from an array.

    Examples:
        .. code-block:: python

          x = fluid.layers.data(name='x', shape=[10])
          table = fluid.layers.lod_rank_table(x, level=0)
          array = fluid.layers.lod_tensor_to_array(x, table)
          lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
    """
    helper = LayerHelper("array_to_lod_tensor", **locals())
    tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type="array_to_lod_tensor",
        inputs={'X': x,
                'RankTable': table},
        outputs={'Out': tmp})
    return tmp


def increment(x, value=1.0, in_place=True):
    """
    This function performs an operation that increments the value in the
    input :math:`x` by an amount: :math:`value` as mentioned in the input
    parameter. This operation is performed in-place by default. Notice that
    the number of elements in :math:`x` must be equal to 1.

    Args:
        x (Variable|list): The tensor that has the input values.
        value (float): The amount by which the values should be incremented.
        in_place (bool): If the increment should be performed in-place.

    Returns:
        Variable: The elementwise-incremented object.

    Examples:
        .. code-block:: python

          data = fluid.layers.data(name='data', shape=[1], dtype='float32',
                                   append_batch_size=False)
          data = fluid.layers.increment(x=data, value=3.0, in_place=True)
    """
    helper = LayerHelper("increment", **locals())
    if not in_place:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        out = x
    helper.append_op(
        type='increment',
        inputs={'X': [x]},
        outputs={'Out': [out]},
        attrs={'step': float(value)})
    return out


def array_write(x, i, array=None):
    """
    This function writes the given input variable to the specified position
    indicating by the arrary index to an output LOD_TENSOR_ARRAY. If the
    output LOD_TENSOR_ARRAY is not given(None), a new one will be created and
    returned.

    Args:
        x (Variable|list): The input tensor from which the data will be read.
        i (Variable|list): The index of the output LOD_TENSOR_ARRAY, pointing to
                           the position to which the input tensor will be
                           written.
        array (Variable|list): The output LOD_TENSOR_ARRAY to which the input
                               tensor will be written. If this parameter is
                               NONE, a new LOD_TENSOR_ARRAY will be created and
                               returned.

    Returns:
        Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.

    Examples:
        .. code-block:: python

          tmp = fluid.layers.zeros(shape=[10], dtype='int32')
          i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
          arr = layers.array_write(tmp, i=i)
    """
    helper = LayerHelper('array_write', **locals())
    if array is None:
        array = helper.create_variable(
            name="{0}.out".format(helper.name),
            type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
            dtype=x.dtype)
    helper.append_op(
        type='write_to_array',
        inputs={'X': [x],
                'I': [i]},
        outputs={'Out': [array]})
    return array


def create_array(dtype):
    """
    **Create LoDTensorArray**

    This function creates an array of LOD_TENSOR_ARRAY . It is mainly used to
    implement RNN with array_write, array_read and While.

    Args:
        dtype (int|float): The data type of the elements in the lod_tensor_array.

    Returns:
        Variable: The lod_tensor_array variable storing the elements of data type.

    Examples:
        .. code-block:: python

          data = fluid.layers.create_array(dtype='float32')

    """
    helper = LayerHelper("array", **locals())
    return helper.create_variable(
        name="{0}.out".format(helper.name),
        type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
        dtype=dtype)


@templatedoc()
def less_than(x, y, force_cpu=None, cond=None):
    """
    ${comment}

    >>> import paddle.fluid as fluid
    >>> less = fluid.layers.less_than(x=label, y=limit)

    Args:
        x(${x_type}): ${x_comment}.
        y(${y_type}): ${y_comment}.
        force_cpu(${force_cpu_type}): ${force_cpu_comment}.
        cond(Variable|None): Optional output variable to store the result of *less_than*

    Returns:
        ${out_comment}.
    """
    helper = LayerHelper("less_than", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    attrs = dict()
    if force_cpu is not None:
        attrs['force_cpu'] = force_cpu
    elif force_init_on_cpu():
        attrs['force_cpu'] = force_init_on_cpu()

    helper.append_op(
        type='less_than',
        inputs={'X': [x],
                'Y': [y]},
        outputs={'Out': [cond]},
        attrs=attrs)
    return cond


@templatedoc()
def less_equal(x, y, cond=None):
    """
    This layer returns the truth value of :math:`x <= y` elementwise, which is equivalent to the overloaded operator `<=`.

    Args:
        x(Variable): First operand of *less_equal*
        y(Variable): Second operand of *less_equal*
        cond(Variable|None): Optional output variable to store the result of *less_equal*

    Returns:
        Variable: The tensor variable storing the output of *less_equal*.

    Examples:
        .. code-block:: python

          out = fluid.layers.less_equal(x=label, y=limit)
    """
    helper = LayerHelper("less_equal", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    attrs = dict()
    if force_init_on_cpu():
        attrs['force_cpu'] = force_init_on_cpu()

    helper.append_op(
        type='less_equal',
        inputs={'X': [x],
                'Y': [y]},
        outputs={'Out': [cond]},
        attrs=attrs)
    return cond


@templatedoc()
def greater_than(x, y, cond=None):
    """
    This layer returns the truth value of :math:`x > y` elementwise, which is equivalent to the overloaded operator `>`.

    Args:
        x(Variable): First operand of *greater_than*
        y(Variable): Second operand of *greater_than*
        cond(Variable|None): Optional output variable to store the result of *greater_than*

    Returns:
        Variable: The tensor variable storing the output of *greater_than*.

    Examples:
        .. code-block:: python

          out = fluid.layers.greater_than(x=label, y=limit)
    """
    helper = LayerHelper("greater_than", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    attrs = dict()
    if force_init_on_cpu():
        attrs['force_cpu'] = force_init_on_cpu()

    helper.append_op(
        type='greater_than',
        inputs={'X': [x],
                'Y': [y]},
        outputs={'Out': [cond]},
        attrs=attrs)
    return cond


@templatedoc()
def greater_equal(x, y, cond=None):
    """
    This layer returns the truth value of :math:`x >= y` elementwise, which is equivalent to the overloaded operator `>=`.

    Args:
        x(Variable): First operand of *greater_equal*
        y(Variable): Second operand of *greater_equal*
        cond(Variable|None): Optional output variable to store the result of *greater_equal*

    Returns:
        Variable: The tensor variable storing the output of *greater_equal*.

    Examples:
        .. code-block:: python

          out = fluid.layers.greater_equal(x=label, y=limit)
    """
    helper = LayerHelper("greater_equal", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    attrs = dict()
    if force_init_on_cpu():
        attrs['force_cpu'] = force_init_on_cpu()

    helper.append_op(
        type='greater_equal',
        inputs={'X': [x],
                'Y': [y]},
        outputs={'Out': [cond]},
        attrs=attrs)
    return cond


def equal(x, y, cond=None):
    """
    This layer returns the truth value of :math:`x == y` elementwise.

    Args:
        x(Variable): First operand of *equal*
        y(Variable): Second operand of *equal*
        cond(Variable|None): Optional output variable to store the result of *equal*

    Returns:
        Variable: The tensor variable storing the output of *equal*.

    Examples:
        .. code-block:: python

          less = fluid.layers.equal(x=label, y=limit)
    """
    helper = LayerHelper("equal", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    helper.append_op(
        type='equal', inputs={'X': [x],
                              'Y': [y]}, outputs={'Out': [cond]})
    return cond


def not_equal(x, y, cond=None):
    """
    This layer returns the truth value of :math:`x != y` elementwise, which is equivalent to the overloader operator `!=`.

    Args:
        x(Variable): First operand of *not_equal*
        y(Variable): Second operand of *not_equal*
        cond(Variable|None): Optional output variable to store the result of *not_equal*

    Returns:
        Variable: The tensor variable storing the output of *not_equal*.

    Examples:
        .. code-block:: python

          out = fluid.layers.not_equal(x=label, y=limit)
    """
    helper = LayerHelper("not_equal", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True

    helper.append_op(
        type='not_equal', inputs={'X': [x],
                                  'Y': [y]}, outputs={'Out': [cond]})
    return cond


def array_read(array, i):
    """
    This function performs the operation to read the data in as an
    LOD_TENSOR_ARRAY.

    .. code-block:: text

        Given:

        array = [0.6, 0.1, 0.3, 0.1]

        And:

        i = 2

        Then:

        output = 0.3

    Args:
        array (Variable|list): The input tensor that store data to be read.
        i (Variable|list): The index of the data to be read from input array.

    Returns:
        Variable: The tensor type variable that has the data written to it.

    Examples:
        .. code-block:: python

          array = fluid.layers.create_array(dtype='float32')
          i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
          item = fluid.layers.array_read(array, i)
    """
    helper = LayerHelper('array_read', **locals())
    if not isinstance(
            array,
            Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
        raise TypeError("array should be tensor array vairable")
    out = helper.create_variable_for_type_inference(dtype=array.dtype)
    helper.append_op(
        type='read_from_array',
        inputs={'X': [array],
                'I': [i]},
        outputs={'Out': [out]})
    return out


def shrink_memory(x, i, table):
    """
    This function creates an operator to shrink rnn memory using the RankTable
    as mentioned in the input parameter.

    NOTE: This API is very low-level API. It is used by DynamicRNN only.

    Since the Dynamic RNN uses no-padding way to implement RNN. The sequence
    will be sorted by order, and the length of valid memory will be shrink after
    each time step.

    Args:
        x(Variable): The memory object in the previous time step.
        i(Variable): The step count variable. A int scalar as LoDTensor.
        table(Variable): The RNNRankTable object.

    Returns:
        the memory variable after shrink.

    Examples:

        Since this API is very low level API. The example is not provided.
        Please reference the implementation of class DynamicRNN for detail
        usage.
    """
    helper = LayerHelper('shrink_memory', **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='shrink_rnn_memory',
        inputs={'X': [x],
                'I': [i],
                'RankTable': [table]},
        outputs={'Out': [out]},
        attrs={})
    return out


def array_length(array):
    """
    **Get the Length of Input LoDTensorArray**

    This function performs the operation to find the length of the input
    LOD_TENSOR_ARRAY.

    Related API: array_read, array_write, While.

    Args:
        array (LOD_TENSOR_ARRAY): The input array that will be used
                                  to compute the length.

    Returns:
        Variable: The length of the input LoDTensorArray.

    Examples:
        .. code-block:: python

          tmp = fluid.layers.zeros(shape=[10], dtype='int32')
          i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
          arr = fluid.layers.array_write(tmp, i=i)
          arr_len = fluid.layers.array_length(arr)

    """
    helper = LayerHelper('array_length', **locals())
    tmp = helper.create_variable_for_type_inference(dtype='int64')
    tmp.stop_gradient = True
    helper.append_op(
        type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
    return tmp


class ConditionalBlockGuard(BlockGuard):
    """
    ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
    holding a ConditionalBlock, and helping users entering and exiting the
    ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
    is generally an internal component of IfElse, users should not use it directly.
    """

    def __init__(self, block):
        if not isinstance(block, ConditionalBlock):
            raise TypeError("block should be conditional block")
        super(ConditionalBlockGuard, self).__init__(block.helper.main_program)
        self.block = block

    def __enter__(self):
        return super(ConditionalBlockGuard, self).__enter__()

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.block.complete()
        return super(ConditionalBlockGuard, self).__exit__(exc_type, exc_val,
                                                           exc_tb)


class ConditionalBlock(object):
    '''
    **ConditionalBlock**

    ConditionalBlock is an operator that bind a block to a specific condition,
    if the condition matches, the corresponding block will be executed.

    Args:
        inputs (Variable): bool conditions.
        is_scalar_condition (bool): whether the branch is controled by a scalar.
        name(str): name of this ConditionalBlock.

    Examples:
        .. code-block:: python

             cond = layers.less_than(x=label, y=limit)
             true_image, false_image = layers.split_lod_tensor(
                 input=image, mask=cond)
             true_cond = layers.ConditionalBlock([true_image])

             with true_cond.block():
                 ...
             with false_cond.block():
                 ...
    '''

    def __init__(self, inputs, is_scalar_condition=False, name=None):
        for each_input in inputs:
            if not isinstance(each_input, Variable):
                raise TypeError("Each input should be variable")
        self.inputs = inputs
        self.is_scalar_condition = is_scalar_condition
        self.helper = LayerHelper('conditional_block', name=name)

    def block(self):
        return ConditionalBlockGuard(self)

    def complete(self):
        inside_block = self.helper.main_program.current_block()
        parent_block = self.helper.main_program.block(inside_block.parent_idx)

        intermediate = set()
        params = set()

        for each_op in inside_block.ops:
            assert isinstance(each_op, Operator)
            for iname in each_op.input_names:
                for in_var_name in each_op.input(iname):
                    if in_var_name not in intermediate:
                        params.add(in_var_name)

            for oname in each_op.output_names:
                for out_var_name in each_op.output(oname):
                    intermediate.add(out_var_name)
        input_set = set([ipt.name for ipt in self.inputs])

        param_list = [
            parent_block._var_recursive(each_name) for each_name in params
            if each_name not in input_set
        ]

        out_list = []
        for inner_out_name in intermediate:
            inner_var = parent_block._find_var_recursive(inner_out_name)
            if inner_var:
                out_list.append(inner_var)

        step_scope = parent_block.create_var(
            type=core.VarDesc.VarType.STEP_SCOPES)
        parent_block.append_op(
            type='conditional_block',
            inputs={
                'Cond': self.inputs,
                'Input': param_list,
            },
            outputs={'Out': out_list,
                     'Scope': [step_scope]},
            attrs={
                'sub_block': inside_block,
                'is_scalar_condition': self.is_scalar_condition
            })


class Switch(object):
    """
    Switch class works just like a `if-elif-else`. Can be used in learning rate scheduler
    to modify learning rate

    The Semantics:

    1. A `switch` control-flow checks cases one-by-one.

    2. The condition of each case is a boolean value, which is a scalar Variable.

    3. It runs the first matched case, or the default case if there is one.

    4. Once it matches a case, it runs the corresponding branch and only that branch.

    Examples:
        .. code-block:: python

            lr = fluid.layers.tensor.create_global_var(
                shape=[1],
                value=0.0,
                dtype='float32',
                persistable=True,
                name="learning_rate")
            one_var = tensor.fill_constant(
                shape=[1], dtype='float32', value=1.0)
            two_var = tensor.fill_constant(
                shape=[1], dtype='float32', value=2.0)

            with fluid.layers.control_flow.Switch() as switch:
                with switch.case(global_step == zero_var):
                    fluid.layers.tensor.assign(input=one_var, output=lr)
                with switch.default():
                    fluid.layers.tensor.assign(input=two_var, output=lr)

    """

    def __init__(self, name=None):
        self.helper = LayerHelper('switch', name=name)
        self.inside_scope = False
        self.pre_not_conditions = []

    def case(self, condition):
        """create a new block for this condition
        """
        if not self.inside_scope:
            raise ValueError("case should be called inside with")

        if len(self.pre_not_conditions) == 0:
            cond_block = ConditionalBlock([condition], is_scalar_condition=True)
            not_cond = logical_not(x=condition)
            self.pre_not_conditions.append(not_cond)
        else:
            pre_cond_num = len(self.pre_not_conditions)
            pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
            new_not_cond = logical_and(
                x=pre_not_cond, y=logical_not(x=condition))
            self.pre_not_conditions.append(new_not_cond)
            cond_block = ConditionalBlock(
                [logical_and(
                    x=pre_not_cond, y=condition)],
                is_scalar_condition=True)

        return ConditionalBlockGuard(cond_block)

    def default(self):
        """
        create a default case for this switch
        """
        pre_cond_num = len(self.pre_not_conditions)
        if pre_cond_num == 0:
            raise ValueError("there should be at least one condition")
        cond_block = ConditionalBlock(
            [self.pre_not_conditions[pre_cond_num - 1]],
            is_scalar_condition=True)
        return ConditionalBlockGuard(cond_block)

    def __enter__(self):
        """
        set flag that now is inside switch.block {}
        :return:
        """
        self.inside_scope = True
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.inside_scope = False
        if exc_type is not None:
            return False  # re-raise exception

        return True


class IfElseBlockGuard(object):
    def __init__(self, is_true, ifelse):
        if not isinstance(ifelse, IfElse):
            raise TypeError("ifelse must be an instance of IfElse class")

        if ifelse.status != IfElse.OUT_IF_ELSE_BLOCKS:
            raise ValueError("You cannot invoke IfElse.block() inside a block")

        self.is_true = is_true
        self.ie = ifelse
        if is_true:
            self.cond_block = ifelse.conditional_true_block
        else:
            self.cond_block = ifelse.conditional_false_block

        if not isinstance(self.cond_block, ConditionalBlock):
            raise TypeError("Unexpected situation")

        self.cond_block = self.cond_block.block()

    def __enter__(self):
        self.ie.status = IfElse.IN_IF_ELSE_TRUE_BLOCKS if self.is_true else IfElse.IN_IF_ELSE_FALSE_BLOCKS
        self.cond_block.__enter__()

    def __exit__(self, exc_type, exc_val, exc_tb):
        if not self.cond_block.__exit__(exc_type, exc_val, exc_tb):
            # re-raise inside exception
            return False
        if len(self.ie.output_table[1 if self.is_true else 0]) == 0:
            raise ValueError("Must set output inside block")
        self.ie.status = IfElse.OUT_IF_ELSE_BLOCKS


class IfElse(object):
    """
    if-else control flow.

    Args:
        cond (Variable): condition used to compare.
        name (str, default None): The name of this layer.

    Examples:
          .. code-block:: python

            limit = fluid.layers.fill_constant_batch_size_like(
                input=label, dtype='int64', shape=[1], value=5.0)
            cond = fluid.layers.less_than(x=label, y=limit)
            ie = fluid.layers.IfElse(cond)
            with ie.true_block():
                true_image = ie.input(image)
                hidden = fluid.layers.fc(input=true_image, size=100, act='tanh')
                prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
                ie.output(prob)

            with ie.false_block():
                false_image = ie.input(image)
                hidden = fluid.layers.fc(
                    input=false_image, size=200, act='tanh')
                prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
                ie.output(prob)
            prob = ie()
    """
    OUT_IF_ELSE_BLOCKS = 0
    IN_IF_ELSE_TRUE_BLOCKS = 1
    IN_IF_ELSE_FALSE_BLOCKS = 2

    def __init__(self, cond, name=None):
        if not isinstance(cond, Variable):
            raise TypeError("cond must be a Variable")
        self.helper = LayerHelper('ifelse', name=name)
        self.cond = cond
        self.input_table = {}
        self.status = IfElse.OUT_IF_ELSE_BLOCKS
        self.conditional_true_block = ConditionalBlock(inputs=[self.cond])
        self.conditional_false_block = ConditionalBlock(inputs=[self.cond])
        self.output_table = ([], [])  # (true_outs, false_outs)

    def input(self, x):
        if self.status == IfElse.OUT_IF_ELSE_BLOCKS:
            raise ValueError("input must in true/false blocks")
        if id(x) not in self.input_table:
            parent_block = self._parent_block()
            out_true = parent_block.create_var(
                name=unique_name.generate('ifelse_input' + self.helper.name),
                dtype=x.dtype)

            out_false = parent_block.create_var(
                name=unique_name.generate('ifelse_input' + self.helper.name),
                dtype=x.dtype)
            parent_block.append_op(
                type='split_lod_tensor',
                inputs={
                    'X': x,
                    'Mask': self.cond,
                },
                outputs={'OutTrue': out_true,
                         'OutFalse': out_false},
                attrs={'level': 0})
            self.input_table[id(x)] = (out_true, out_false)
        else:
            out_true, out_false = self.input_table[id(x)]

        if self.status == IfElse.IN_IF_ELSE_TRUE_BLOCKS:
            return out_true
        else:
            return out_false

    def _parent_block(self):
        current_block = self.helper.main_program.current_block()
        return self.helper.main_program.block(current_block.parent_idx)

    def true_block(self):
        return IfElseBlockGuard(True, self)

    def false_block(self):
        return IfElseBlockGuard(False, self)

    def output(self, *outs):
        if self.status == self.OUT_IF_ELSE_BLOCKS:
            raise ValueError("output can only be invoked in the sub-block")

        out_table = self.output_table[1 if self.status ==
                                      self.IN_IF_ELSE_TRUE_BLOCKS else 0]
        parent_block = self._parent_block()
        for each_out in outs:
            if not isinstance(each_out, Variable):
                raise TypeError("Each output should be a variable")
            # create outside tensor
            outside_out = parent_block.create_var(
                name=unique_name.generate("_".join(
                    [self.helper.name, 'output'])),
                dtype=each_out.dtype)
            out_table.append(outside_out)

            # assign local var to outside
            assign(input=each_out, output=outside_out)

    def __call__(self):
        if self.status != self.OUT_IF_ELSE_BLOCKS:
            raise ValueError("IfElse::__call__ must be out of sub-block")
        false_len, true_len = list(map(len, self.output_table))
        if false_len == 0 and true_len == 0:
            raise ValueError("Must invoke true_block/false_block before "
                             "__call__")
        elif false_len != true_len and false_len != 0 and true_len != 0:
            raise ValueError("The output side must be same")
        elif false_len == 0 or true_len == 0:
            return self.output_table[0 if false_len != 0 else 1]

        # else none of false_len/true_len is zero
        # merge together
        rlist = []
        for false_var, true_var in zip(*self.output_table):
            rlist.append(
                merge_lod_tensor(
                    in_true=true_var,
                    in_false=false_var,
                    mask=self.cond,
                    x=self.cond,
                    level=0))
        return rlist


class DynamicRNN(object):
    """
    The dynamic RNN can process a batch of sequence data. The length of each
    sample sequence can be different. This API automatically process them in
    batch.

    The input lod must be set. Please reference `lod_tensor`

    >>> import paddle.fluid as fluid
    >>> data = fluid.layers.data(name='sentence', dtype='int64', lod_level=1)
    >>> embedding = fluid.layers.embedding(input=data, size=[65535, 32],
    >>>                                    is_sparse=True)
    >>>
    >>> drnn = fluid.layers.DynamicRNN()
    >>> with drnn.block():
    >>>     word = drnn.step_input(embedding)
    >>>     prev = drnn.memory(shape=[200])
    >>>     hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
    >>>     drnn.update_memory(prev, hidden)  # set prev to hidden
    >>>     drnn.output(hidden)
    >>>
    >>> # last is the last time step of rnn. It is the encoding result.
    >>> last = fluid.layers.sequence_last_step(drnn())

    The dynamic RNN will unfold sequence into timesteps. Users need to define
    how to process each time step during the :code:`with` block.

    The `memory` is used staging data cross time step. The initial value of
    memory can be zero or another variable.

    The dynamic RNN can mark multiple variables as its output. Use `drnn()` to
    get the output sequence.
    
    NOTES:
        Currently it is not supported that setting is_sparse to True of any 
        layers within DynamicRNN.
    """
    BEFORE_RNN = 0
    IN_RNN = 1
    AFTER_RNN = 2

    def __init__(self, name=None):
        self.helper = LayerHelper('dynamic_rnn', name=name)
        self.status = DynamicRNN.BEFORE_RNN
        self.lod_rank_table = None
        self.max_seq_len = None
        self.step_idx = None
        self.zero_idx = None
        self.mem_dict = dict()
        self.output_array = []
        self.outputs = []
        self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
        self.cond.stop_gradient = False
        self.while_op = While(self.cond)
        self.input_array = []
        self.mem_link = []

    def step_input(self, x, level=0):
        """
        Mark a sequence as a dynamic RNN input.

        Args:
            x(Variable): The input sequence.
            level(int): The level of lod used to split steps. Default: 0.

        Returns:
            The current timestep in the input sequence.
        """
        self._assert_in_rnn_block_("step_input")
        if not isinstance(x, Variable):
            raise TypeError(
                "step_input() can only take a Variable as its input.")
        parent_block = self._parent_block_()
        if self.lod_rank_table is None:
            self.lod_rank_table = parent_block.create_var(
                name=unique_name.generate('lod_rank_table'),
                type=core.VarDesc.VarType.LOD_RANK_TABLE)
            self.lod_rank_table.stop_gradient = True
            parent_block.append_op(
                type='lod_rank_table',
                inputs={"X": x},
                outputs={"Out": self.lod_rank_table},
                attrs={"level": level})
            self.max_seq_len = parent_block.create_var(
                name=unique_name.generate('dynamic_rnn_max_seq_len'),
                dtype='int64')
            self.max_seq_len.stop_gradient = False
            parent_block.append_op(
                type='max_sequence_len',
                inputs={'RankTable': self.lod_rank_table},
                outputs={"Out": self.max_seq_len})
            self.cond.stop_gradient = True
            parent_block.append_op(
                type='less_than',
                inputs={'X': self.step_idx,
                        'Y': self.max_seq_len},
                outputs={'Out': self.cond},
                attrs={'force_cpu': True})

        input_array = parent_block.create_var(
            name=unique_name.generate('dynamic_rnn_input_array'),
            type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
            dtype=x.dtype)
        self.input_array.append((input_array, x.dtype))
        parent_block.append_op(
            type='lod_tensor_to_array',
            inputs={'X': x,
                    'RankTable': self.lod_rank_table},
            outputs={'Out': input_array})
        return array_read(array=input_array, i=self.step_idx)

    def static_input(self, x):
        """
        Mark a variable as a RNN input. The input will not be scattered into
        time steps.

        Args:
            x(Variable): The input variable.

        Returns:
            The input variable that can access in RNN.
        """
        self._assert_in_rnn_block_("static_input")
        if not isinstance(x, Variable):
            raise TypeError(
                "static_input() can only take a Variable as its input")
        if self.lod_rank_table is None:
            raise RuntimeError(
                "static_input() must be called after step_input().")
        parent_block = self._parent_block_()
        x_reordered = parent_block.create_var(
            name=unique_name.generate("dynamic_rnn_static_input_reordered"),
            type=core.VarDesc.VarType.LOD_TENSOR,
            dtype=x.dtype)
        parent_block.append_op(
            type='reorder_lod_tensor_by_rank',
            inputs={'X': [x],
                    'RankTable': [self.lod_rank_table]},
            outputs={'Out': [x_reordered]})
        return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)

    @signature_safe_contextmanager
    def block(self):
        """
        The block for user to define operators in RNN.
        """
        if self.status != DynamicRNN.BEFORE_RNN:
            raise ValueError("rnn.block() can only be invoke once")
        self.step_idx = fill_constant(
            shape=[1], dtype='int64', value=0, force_cpu=True)
        self.step_idx.stop_gradient = False
        self.status = DynamicRNN.IN_RNN
        with self.while_op.block():
            yield
            increment(x=self.step_idx, value=1.0, in_place=True)

            for new_mem, mem_array in self.mem_link:
                array_write(x=new_mem, i=self.step_idx, array=mem_array)

            less_than(
                x=self.step_idx,
                y=self.max_seq_len,
                force_cpu=True,
                cond=self.cond)

        self.status = DynamicRNN.AFTER_RNN
        for each_array in self.output_array:
            self.outputs.append(
                array_to_lod_tensor(
                    x=each_array, table=self.lod_rank_table))

    def __call__(self, *args, **kwargs):
        """
        Get the output of RNN. This API should only be invoked after RNN.block()
        """
        if self.status != DynamicRNN.AFTER_RNN:
            raise ValueError(("Output of the dynamic RNN can only be visited "
                              "outside the rnn block."))
        if len(self.outputs) == 1:
            return self.outputs[0]
        else:
            return self.outputs

    def memory(self,
               init=None,
               shape=None,
               value=0.0,
               need_reorder=False,
               dtype='float32'):
        """
        Create a memory variable for dynamic rnn.

        If the :code:`init` is not None, :code:`memory` will be initialized by
        this variable. The :code:`need_reorder` is used to reorder the memory as
        the input variable. It should be set to true when the initialized memory
        depends on the input sample.

        For example,

        >>> import paddle.fluid as fluid
        >>> sentence = fluid.layers.data(
        >>>                 name='sentence', dtype='float32', shape=[32])
        >>> boot_memory = fluid.layers.data(
        >>>                 name='boot', dtype='float32', shape=[10])
        >>>
        >>> drnn = fluid.layers.DynamicRNN()
        >>> with drnn.block():
        >>>     word = drnn.step_input(sentence)
        >>>     memory = drnn.memory(init=boot_memory, need_reorder=True)
        >>>     hidden = fluid.layers.fc(
        >>>                 input=[word, memory], size=10, act='tanh')
        >>>     drnn.update_memory(ex_mem=memory, new_mem=hidden)
        >>>     drnn.output(hidden)
        >>> rnn_output = drnn()


        Otherwise, if :code:`shape`, :code:`value`, :code:`dtype` are set, the
        :code:`memory` will be initialized by this :code:`value`.

        For example,

        >>> import paddle.fluid as fluid
        >>> sentence = fluid.layers.data(
        >>>                 name='sentence', dtype='float32', shape=[32])
        >>>
        >>> drnn = fluid.layers.DynamicRNN()
        >>> with drnn.block():
        >>>     word = drnn.step_input(sentence)
        >>>     memory = drnn.memory(shape=[10], dtype='float32', value=0)
        >>>     hidden = fluid.layers.fc(
        >>>             input=[word, memory], size=10, act='tanh')
        >>>     drnn.update_memory(ex_mem=memory, new_mem=hidden)
        >>>     drnn.output(hidden)
        >>> rnn_output = drnn()


        Args:
            init(Variable|None): The initialized variable.

            shape(list|tuple): The memory shape. NOTE the shape does not contain batch_size.

            value(float): the initalized value.

            need_reorder(bool): True if the initialized memory depends on the input sample.

            dtype(str|numpy.dtype): The data type of the initialized memory.

        Returns:
            The memory variable.
        """
        self._assert_in_rnn_block_('memory')
        self._init_zero_idx_()
        if init is not None:
            if not isinstance(init, Variable):
                raise TypeError(
                    "The input arg `init` of memory() must be a Variable")
            parent_block = self._parent_block_()
            init_tensor = init
            if need_reorder == True:
                if self.lod_rank_table is None:
                    raise ValueError(
                        'If set need_reorder to True, make sure step_input be '
                        'invoked before '
                        'memory(init=init, need_reordered=True, ...).')
                init_reordered = parent_block.create_var(
                    name=unique_name.generate('dynamic_rnn_mem_init_reordered'),
                    type=core.VarDesc.VarType.LOD_TENSOR,
                    dtype=init.dtype)
                parent_block.append_op(
                    type='reorder_lod_tensor_by_rank',
                    inputs={
                        'X': [init_tensor],
                        'RankTable': [self.lod_rank_table]
                    },
                    outputs={'Out': [init_reordered]})
                init_tensor = init_reordered
            mem_array = parent_block.create_var(
                name=unique_name.generate('dynamic_rnn_mem_array'),
                type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
                dtype=init.dtype)
            parent_block.append_op(
                type='write_to_array',
                inputs={'X': init_tensor,
                        'I': self.zero_idx},
                outputs={'Out': mem_array})
            retv = array_read(array=mem_array, i=self.step_idx)
            retv = shrink_memory(
                x=retv, i=self.step_idx, table=self.lod_rank_table)
            self.mem_dict[retv.name] = mem_array
            return retv
        else:
            if len(self.input_array) == 0:
                raise ValueError(
                    "step_input should be invoked before memory(shape=..., value=...)"
                )
            parent_block = self._parent_block_()
            init = parent_block.create_var(
                name=unique_name.generate('mem_init'), dtype=dtype)
            arr, dtype = self.input_array[0]
            in0 = parent_block.create_var(
                name=unique_name.generate('in0'), dtype=dtype)
            parent_block.append_op(
                type='read_from_array',
                inputs={'X': [arr],
                        'I': [self.zero_idx]},
                outputs={'Out': [in0]})
            parent_block.append_op(
                type='fill_constant_batch_size_like',
                inputs={'Input': [in0]},
                outputs={'Out': [init]},
                attrs={
                    'shape': [-1] + shape,
                    'value': float(value),
                    'dtype': init.dtype
                })
            return self.memory(init=init)

    def update_memory(self, ex_mem, new_mem):
        """
        Update the memory from ex_mem to new_mem. NOTE that the shape and data
        type of :code:`ex_mem` and :code:`new_mem` must be same.
        
        Args:
            ex_mem(Variable): the memory variable.
            new_mem(Variable): the plain variable generated in RNN block.

        Returns:
            None
        """
        self._assert_in_rnn_block_('update_memory')
        if not isinstance(ex_mem, Variable):
            raise TypeError("The input arg `ex_mem` of update_memory() must "
                            "be a Variable")
        if not isinstance(new_mem, Variable):
            raise TypeError("The input arg `new_mem` of update_memory() must "
                            "be a Variable")

        mem_array = self.mem_dict.get(ex_mem.name, None)
        if mem_array is None:
            raise ValueError("Please invoke memory before update_memory")
        if self.lod_rank_table is None:
            raise ValueError("Please invoke step_input before update_memory")

        self.mem_link.append((new_mem, mem_array))

    def output(self, *outputs):
        """
        Mark the RNN output variables.

        Args:
            outputs: The output variables.

        Returns:
            None
        """
        self._assert_in_rnn_block_('output')
        parent_block = self._parent_block_()
        for each in outputs:
            outside_array = parent_block.create_var(
                name=unique_name.generate("_".join(
                    [self.helper.name, "output_array", each.name])),
                type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
                dtype=each.dtype)
            array_write(x=each, i=self.step_idx, array=outside_array)
            self.output_array.append(outside_array)

    def _init_zero_idx_(self):
        if self.zero_idx is None:
            parent_block = self._parent_block_()
            self.zero_idx = parent_block.create_var(
                name=unique_name.generate('zero_idx'), dtype='int64')
            parent_block.append_op(
                type='fill_constant',
                inputs={},
                outputs={'Out': [self.zero_idx]},
                attrs={
                    'shape': [1],
                    'dtype': self.zero_idx.dtype,
                    'value': float(0),
                    'force_cpu': True
                })

    def _parent_block_(self):
        prog = self.helper.main_program
        parent_idx = prog.current_block().parent_idx
        assert parent_idx >= 0
        parent_block = prog.block(parent_idx)

        return parent_block

    def _assert_in_rnn_block_(self, method):
        if self.status != DynamicRNN.IN_RNN:
            raise ValueError("{0} can only be invoked inside rnn block.".format(
                method))


@autodoc()
def reorder_lod_tensor_by_rank(x, rank_table):
    helper = LayerHelper('reorder_lod_tensor_by_rank', **locals())
    helper.is_instance('x', Variable)
    helper.is_instance('rank_table', Variable)

    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='reorder_lod_tensor_by_rank',
        inputs={'X': [x],
                'RankTable': [rank_table]},
        outputs={'Out': [out]})
    return out


def is_empty(x, cond=None):
    """
    Test whether a Variable is empty.

    Args:
        x (Variable): The Variable to be tested.
        cond (Variable|None): Output parameter. Returns the test result
                              of given 'x'. Default: None

    Returns:
        Variable: A bool scalar. True if 'x' is an empty Variable.

    Raises:
        TypeError: If input cond is not a variable, or cond's dtype is
                   not bool.

    Examples:
        .. code-block:: python

          res = fluid.layers.is_empty(x=input)
          # or:
          fluid.layers.is_empty(x=input, cond=res)
    """
    helper = LayerHelper("is_empty", **locals())
    if cond is None:
        cond = helper.create_variable_for_type_inference(dtype='bool')
        cond.stop_gradient = True
    elif not isinstance(cond, Variable):
        raise TypeError("cond takes a variable")
    elif cond.dtype != 'bool':
        raise TypeError("The data type of cond must be bool")

    helper.append_op(
        type='is_empty', inputs={'X': [x]}, outputs={'Out': [cond]})
    return cond