control_flow.py

#   Copyright (c) 2022 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.

import warnings
from functools import partial, reduce

import paddle
import paddle.fluid.core as core
from paddle.common_ops_import import (
    LayerHelper,
    _non_static_mode,
    check_type,
    check_variable_and_dtype,
    convert_dtype,
)
from paddle.fluid.framework import Operator, Program, Variable

# Temporary solution, it will be deleted later
from paddle.fluid.layers.control_flow import cond
from paddle.fluid.layers.utils import (
    assert_same_structure,
    copy_mutable_vars,
    hold_mutable_vars,
    is_sequence,
    map_structure,
)


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

    def __enter__(self):
        self.while_op.status = While.IN_WHILE_BLOCK
        return super().__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().__exit__(exc_type, exc_val, exc_tb)


def get_inputs_outputs_in_block(
    current_block, inner_inputs, inner_outputs, helper
):
    """
    Find inputs and outputs in current control flow block.
    :param current_block: Current control flow block.
    :param inner_inputs: Input var name of ops in current block.
    :param inner_outputs: Output var name of ops in current block.
    :return: inner_inputs, inner_outputs
    """

    def is_ignore_vars(op, var_name):
        # NOTE(dev): There are some persistable var created in some non-standard API
        # such as "contrib.layers.shuffle_batch". It create a "Seed" used both in
        # Input and Output. This var shall not be considered as a loop_var in
        # control_flow.
        IGNORE_VAR_NAMES = {"shuffle_batch": ["shuffle_batch_seed"]}
        if op.type in IGNORE_VAR_NAMES:
            var_names = IGNORE_VAR_NAMES[op.type]
            for name in var_names:
                if name in var_name:
                    return True
        return False

    # Step1: update inner_inputs and inner_outputs
    # NOTE: Here assumes that all variables are input or output of Ops,
    # but some variables are created without appendding a real op.
    # For example, in `arr = create_array(dtype)`, `arr` is not a output of a op.
    for op in current_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 inner_outputs and not is_ignore_vars(
                    op, in_var_name
                ):
                    inner_inputs.add(in_var_name)

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

    # Step2: Remove LOD_TENSOR_ARRAY created in current control flow block.
    remove_inner_inputs = set()
    parent_block = helper.main_program.block(current_block.parent_idx)

    for in_var_name in inner_inputs:
        parent_block_var = parent_block._find_var_recursive(in_var_name)
        current_block_var = None
        if current_block.has_var(in_var_name):
            current_block_var = current_block.var(in_var_name)
        if (
            not parent_block_var
            and current_block_var
            and current_block_var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY
        ):
            remove_inner_inputs.add(in_var_name)

    inner_inputs = inner_inputs - remove_inner_inputs

    return inner_inputs, inner_outputs


class While:
    """
    :api_attr: Static Graph

    while loop control flow. Repeat while body until cond is False.

    Note:
        A new OP :ref:`api_fluid_layers_while_loop` is highly recommended instead of ``While`` if the shape of parameter ``cond`` is [1].
        OP :ref:`api_fluid_layers_while_loop` is easier to use and is called with less code but does the same thing as ``While`` .

    Notice:
        Local variables created in ``While`` are similar to that created in while of C++, and cannot be referenced externally.
        As a result, they cannot be obtained through ``fetch_list`` of ``Executor``. If you would like to access the variable
        out of ``while`` , PaddlePaddle provides ``assign`` API to assign local variables to external. Please refer to example
        code 2 or refer to `issue#22724 <https://github.com/PaddlePaddle/Paddle/issues/22724>`_.

    Args:
        cond(Variable): A Tensor whose data type is bool controlling whether to continue looping.
        is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False.
        name(str, optional): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name` .

    Examples 1:
          .. code-block:: python

            import paddle
            import numpy as np

            paddle.enable_static()

            i = paddle.full(shape=[1], dtype='int64', fill_value=0)           # loop counter

            loop_len = paddle.full(shape=[1],dtype='int64', fill_value=10)    # loop length

            cond = paddle.less_than(x=i, y=loop_len)
            while_op = paddle.static.nn.control_flow.While(cond=cond)
            with while_op.block():
                i = paddle.increment(x=i, value=1)
                paddle.assign(paddle.less_than(x=i, y=loop_len), output=cond)

            exe = paddle.static.Executor(paddle.CPUPlace())
            exe.run(paddle.static.default_startup_program())

            res = exe.run(paddle.static.default_main_program(), feed={}, fetch_list=[i])
            print(res) # [array([10])]


    Examples 2:
          .. code-block:: python

            import paddle
            import numpy as np

            paddle.enable_static()

            i = paddle.full(shape=[1], dtype='int64', fill_value=0)
            loop_len = paddle.full(shape=[1], dtype='int64', fill_value=10)
            one = paddle.full(shape=[1], dtype='float32', fill_value=1)
            data = paddle.static.data(name='data', shape=[1], dtype='float32')
            sums = paddle.full(shape=[1], dtype='float32', fill_value=0)  # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained

            cond = paddle.less_than(x=i, y=loop_len)
            while_op = paddle.static.nn.control_flow.While(cond=cond)
            with while_op.block():
                sums_tensor = paddle.add(x=data, y=data)
                paddle.assign(sums_tensor, sums)  # Update the value of sums_tensor defined in While to the sums which defined outside of While through layers.assign
                i = paddle.increment(x=i, value=1)
                data = paddle.add(x=data, y=one)
                paddle.assign(paddle.less_than(x=i, y=loop_len), output=cond)

            feed_data = np.ones(1).astype('float32')
            exe = paddle.static.Executor(paddle.CPUPlace())
            exe.run(paddle.static.default_startup_program())
            res = exe.run(paddle.static.default_main_program(), feed={'data': feed_data}, fetch_list=sums)
            print(res[0])  # [2.]    # Because the data in While does not update the value outside the While, the value of sums is [2.] after the loop
    """

    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
        check_variable_and_dtype(cond, 'cond', ['bool'], 'static.nn.While')
        if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
            raise TypeError(
                "condition expected shape as [1], but given shape as {0}.".format(
                    list(cond.shape)
                )
            )
        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()
        x_name_list, inner_outputs = get_inputs_outputs_in_block(
            while_block, x_name_list, inner_outputs, self.helper
        )

        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)

        x_name_list |= set(map(lambda x: x.name, out_vars))
        # NOTE(dev): cond_var has been contained in Input('Condition'), so
        # we remove it from Input('X')
        x_name_list -= {self.cond_var.name}

        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},
        )


support_ret_buildin_type = (bool, float, int)


def assign_skip_lod_tensor_array(input, output):
    """
    Assign input to output, but skip the process of copying LoDTensorArray unless it's created in while_block.
    """

    def has_shape_diff(x_var, y_var):
        if len(x_var.shape) != len(y_var.shape):
            return True
        for x_dim, y_dim in zip(x_var.shape, y_var.shape):
            if x_dim != y_dim and -1 not in [x_dim, y_dim]:
                return True
        return False

    if not isinstance(input, (Variable, core.VarBase)):
        if isinstance(output, Variable) and isinstance(
            input, support_ret_buildin_type
        ):
            paddle.assign(input, output)
        else:
            output = input
        return

    if input.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
        main_program = input.block.program
        parent_block = main_program.block(
            main_program.current_block().parent_idx
        )
        if parent_block and not parent_block._find_var_recursive(input.name):
            paddle.assign(input, output)
    else:
        if (
            isinstance(output, Variable)
            and isinstance(input, Variable)
            and has_shape_diff(input, output)
        ):
            warnings.warn(
                "In dy2static mode, we attemp to assign a variable with shape {} into a variable with shape{}, which is not always right.".format(
                    input.shape, output.shape
                )
            )
        paddle.assign(input, output)


def while_loop(cond, body, loop_vars, is_test=False, name=None):
    """
    :api_attr: Static Graph

    while_loop is one of the control flows. Repeats while_loop `body` until `cond` returns False.

    Notice:
        Local variables defined in ``body`` cannot be obtained through ``fetch_list`` of ``Executor`` , variables should
        be defined outside ``body`` and placed in ``loop_vars`` for looping, then these variables can be fetched by ``fetch_list`` .

    Args:
        cond(Callable): A callable returning a boolean tensor controlling whether to continue looping. And ``cond`` takes
            as many arguments as ``loop_vars`` .
        body(Callable): A callable returning a tuple or list of tensors or LoDTensorArrays of the same arity
            (length and structure) and types as ``loops_vars`` . And ``body`` takes as many arguments as ``loop_vars`` .
        loop_vars(list|tuple): A list or tuple of tensors or LoDTensorArrays that is passed to both ``cond`` and ``body`` .
        is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False.
        name(str, optional): Normally there is no need for users to set this property. For more information, please
            refer to :ref:`api_guide_Name`. Default is None.

    Returns:
        A list or tuple of Tensors or LoDTensorArrays which returned by ``body`` .

    Examples:
        .. code-block:: python

            import paddle
            paddle.enable_static()

            def cond(i, ten):
                return i < ten

            def body(i, ten):
                i = i + 1
                return [i, ten]

            main_program = paddle.static.default_main_program()
            startup_program = paddle.static.default_startup_program()
            with paddle.static.program_guard(main_program, startup_program):
                i = paddle.full(shape=[1], fill_value=0, dtype='int64')     # loop counter
                ten = paddle.full(shape=[1], fill_value=10, dtype='int64')  # loop length
                i, ten = paddle.static.nn.while_loop(cond, body, [i, ten])

                exe = paddle.static.Executor(paddle.CPUPlace())
                res = exe.run(main_program, feed={}, fetch_list=[i])
                print(res) # [array([10])]
    """
    helper = LayerHelper('while_loop', **locals())

    if not callable(cond):
        raise TypeError("cond in while_loop should be callable")
    if not callable(body):
        raise TypeError("body in while_loop should be callable")
    check_type(loop_vars, 'loop_vars', (list, tuple), 'static.nn.while_loop')
    if len(loop_vars) == 0:
        raise ValueError("loop_vars in while_loop should not be empty")

    pre_cond = cond(*loop_vars)
    check_variable_and_dtype(
        pre_cond, 'var of cond returned', ['bool'], 'static.nn.while_loop'
    )
    if reduce(lambda a, b: a * b, pre_cond.shape, 1) != 1:
        raise TypeError(
            "the shape of the variable returned by cond should be [1],"
            "but given shape as {0}.".format(list(pre_cond.shape))
        )

    if _non_static_mode():
        now_cond = pre_cond.numpy()[0]
        while now_cond:
            output_vars = body(*loop_vars)
            if not isinstance(output_vars, (list, tuple)):
                output_vars = [output_vars]
            if len(output_vars) != len(loop_vars):
                raise ValueError(
                    "body in while_loop should return the same arity "
                    "(length and structure) and types as loop_vars"
                )
            now_cond = cond(*output_vars).numpy()[0]
            map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars)
        return loop_vars

    while_loop_block = While(pre_cond, is_test, name)
    has_mutable_vars_in_loop = hold_mutable_vars(loop_vars)
    with while_loop_block.block():
        # If a variable with mutable type is included in loop_vars, like `dict/list`,
        # modifying it in the body function will cause origin variable to be modified
        # synchronously. This will raise an assignment error out of while block.
        # Here we make a copy of the mutable vars to avoid this problem.
        if has_mutable_vars_in_loop:
            new_loop_vars = copy_mutable_vars(loop_vars)
            output_vars = body(*new_loop_vars)
        else:
            output_vars = body(*loop_vars)
        if not isinstance(output_vars, (list, tuple)):
            output_vars = [output_vars]
        try:
            loop_vars = _deal_with_undefined_var(output_vars, loop_vars)
            assert_same_structure(output_vars, loop_vars, check_types=False)
        except ValueError as e:
            raise ValueError(
                "body in while_loop should return the same arity "
                "(length and structure) as loop_vars: {0}".format(e)
            )
        now_cond = cond(*output_vars)
        map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars)
        paddle.assign(now_cond, pre_cond)
    return loop_vars


def _deal_with_undefined_var(output_vars, loop_vars):
    """Deal with undefined var cases, We create undefined variable based on the results of body().
    In Dy2Static, we use undefined var to represent the var created in control flow. This function
    expand the loop_vars and replace original loop_vars.
    1. UndefinedVar = Variable      # create a variable
    2. UndefinedVar = None          # create a undefined var with RETURN_NO_VALUE_MAGIC_NUM
    3. UndefinedVar = List(int)     # create a list of variable
    4. UndefinedVar = value         # create a variable
    """
    from paddle.jit.dy2static.utils import (
        UndefinedVar,
        create_undefined_variable,
    )

    def create_var_like(o_var):
        if (
            isinstance(o_var, (Variable,) + support_ret_buildin_type)
            or o_var is None
        ):
            return create_undefined_variable()
        if is_sequence(o_var):
            """
            Create a complex container class inside the body of while, including Python list and python Dict
            """
            return map_structure(lambda x: create_undefined_variable(), o_var)

    if len(output_vars) != len(loop_vars):
        raise ValueError("The length of loop_vars should be the same.")

    results = []
    for o_var, l_var in zip(output_vars, loop_vars):
        if isinstance(l_var, UndefinedVar) or l_var is None:
            results.append(create_var_like(o_var))
        else:
            results.append(l_var)
    return results


def _error_message(what, arg_name, op_name, right_value, error_value):
    error_message = (
        "{what} of '{arg_name}' in {op_name} must be "
        "{right_value}, but received: {error_value}.".format(
            what=what,
            arg_name=arg_name,
            op_name=op_name,
            right_value=right_value,
            error_value=error_value,
        )
    )

    return error_message


def case(pred_fn_pairs, default=None, name=None):
    '''
    :api_attr: Static Graph

    This operator works like an if-elif-elif-else chain.

    Args:
        pred_fn_pairs(list|tuple): A list or tuple of (pred, fn) pairs. ``pred`` is a boolean Tensor with shape [1], ``fn`` is a callable. All callables return the same structure of Tensors.
        default(callable, optional): Callable that returns a structure of Tensors.
        name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor|list(Tensor): Tensors returned by the callable from the first pair whose pred is True,
        or Tensors returned by ``default`` if no pred in ``pred_fn_pairs`` is True and ``default`` is not None,
        or Tensors returned by the last callable in ``pred_fn_pairs``  if no pred in ``pred_fn_pairs`` is True and ``default`` is None.

    Raises:
        TypeError: If the type of ``pred_fn_pairs`` is not list or tuple.
        TypeError: If the type of elements in ``pred_fn_pairs`` is not tuple.
        TypeError: If the size of tuples in ``pred_fn_pairs`` is not 2.
        TypeError: If the first element of 2-tuple in ``pred_fn_pairs`` is not a Tensor.
        TypeError: If the second element of 2-tuple in ``pred_fn_pairs`` is not callable.
        TypeError: If ``default`` is not None but it is not callable.

    Examples:
        .. code-block:: python

            import paddle

            paddle.enable_static()

            def fn_1():
                return paddle.full(shape=[1, 2], dtype='float32', fill_value=1)

            def fn_2():
                return paddle.full(shape=[2, 2], dtype='int32', fill_value=2)

            def fn_3():
                return paddle.full(shape=[3], dtype='int32', fill_value=3)

            main_program = paddle.static.default_startup_program()
            startup_program = paddle.static.default_main_program()

            with paddle.static.program_guard(main_program, startup_program):
                x = paddle.full(shape=[1], dtype='float32', fill_value=0.3)
                y = paddle.full(shape=[1], dtype='float32', fill_value=0.1)
                z = paddle.full(shape=[1], dtype='float32', fill_value=0.2)

                pred_1 = paddle.less_than(z, x)  # true: 0.2 < 0.3
                pred_2 = paddle.less_than(x, y)  # false: 0.3 < 0.1
                pred_3 = paddle.equal(x, y)      # false: 0.3 == 0.1

                # Call fn_1 because pred_1 is True
                out_1 = paddle.static.nn.case(
                    pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3)

                # Argument default is None and no pred in pred_fn_pairs is True. fn_3 will be called.
                # because fn_3 is the last callable in pred_fn_pairs.
                out_2 = paddle.static.nn.case(pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)])

                exe = paddle.static.Executor(paddle.CPUPlace())
                res_1, res_2 = exe.run(main_program, fetch_list=[out_1, out_2])
                print(res_1)  # [[1. 1.]]
                print(res_2)  # [3 3 3]
    '''
    helper = LayerHelper('case', **locals())

    def _case_check_args(pred_fn_pairs, default):
        '''
        Check arguments pred_fn_pairs and default. Return canonical pre_fn_pairs and default.
        '''
        check_type(pred_fn_pairs, 'pred_fn_pairs', (list, tuple), 'case')

        for pred_fn in pred_fn_pairs:
            if not isinstance(pred_fn, tuple):
                raise TypeError(
                    _error_message(
                        "The elements' type",
                        "pred_fn_pairs",
                        "case",
                        tuple,
                        type(pred_fn),
                    )
                )
            if len(pred_fn) != 2:
                raise TypeError(
                    _error_message(
                        "The tuple's size",
                        "pred_fn_pairs",
                        "case",
                        "2",
                        str(len(pred_fn)) + "-tuple",
                    )
                )
            pred, fn = pred_fn

            if not isinstance(pred, Variable):
                raise TypeError(
                    _error_message(
                        "The pred's type",
                        "pred_fn_pairs",
                        "case",
                        "boolean Variable",
                        type(pred),
                    )
                )

            if not callable(fn):
                raise TypeError(
                    "The fn for {} of pred_fn_pairs in Op(case) must"
                    " be callable.".format(pred.name)
                )

        if default is None:
            default_index = len(pred_fn_pairs) - 1  # pick the last one
            default = pred_fn_pairs[default_index][1]
            pred_fn_pairs = pred_fn_pairs[:default_index]
        elif not callable(default):
            raise TypeError("The default in Op(case) must be callable.")

        return pred_fn_pairs, default

    pred_fn_pairs, default = _case_check_args(pred_fn_pairs, default)

    false_fn = default
    for pred, true_fn in reversed(pred_fn_pairs):
        false_fn = partial(cond, pred=pred, true_fn=true_fn, false_fn=false_fn)

    final_fn = false_fn

    return final_fn()


def switch_case(branch_index, branch_fns, default=None, name=None):
    '''
    :api_attr: Static Graph

    This operator is like a C++ switch/case statement.

    Args:
        branch_index(Tensor): A Tensor with shape [1] to specify which branch to execute. The data type is ``int32``, ``int64`` or ``uint8``.
        branch_fns(dict|list|tuple): If it's a list or tuple, the elements in it could be pairs of (int, callable) or simple callables whose actual index will be used as the index of callable. If it's a dict, its key is a python integer and the value is a callable. All callables return the same structure of Tensors.
        default(callable, optional): Callable that returns a structure of Tensors.
        name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor|list(Tensor): Tensors returned by the callable specified by ``branch_index`` in ``branch_fns``,
        or Tensors returned by ``default`` if ``default`` is not None and no index matches in ``branch_fns``,
        or Tensors returned by the callable with the max index in ``branch_fns`` if ``default`` is None and no index matches in ``branch_fns``.

    Raises:
        TypeError: If the type of ``branch_index`` is not Tensor.
        TypeError: If the data type of ``branch_index`` is not ``int32``, ``int64`` or ``uint8``.
        TypeError: If the type of ``branch_fns`` is not dict, list or tuple.
        TypeError: If the elements of ``branch_fns`` is not 2-tuple.
        TypeError: If the first element of 2-tuple in ``branch_fns`` is not integer.
        ValueError: If the first element of 2-tuple in ``branch_fns`` is not unique.
        TypeError: If the second element of 2-tuple in ``branch_fns`` is not callable.
        TypeError: If ``default`` is not None but it is not callable.

    Examples:
        .. code-block:: python

            import paddle

            paddle.enable_static()

            def fn_1():
                return paddle.full(shape=[1, 2], dtype='float32', fill_value=1)

            def fn_2():
                return paddle.full(shape=[2, 2], dtype='int32', fill_value=2)

            def fn_3():
                return paddle.full(shape=[3], dtype='int32', fill_value=3)

            main_program = paddle.static.default_startup_program()
            startup_program = paddle.static.default_main_program()
            with paddle.static.program_guard(main_program, startup_program):
                index_1 = paddle.full(shape=[1], dtype='int32', fill_value=1)
                index_2 = paddle.full(shape=[1], dtype='int32', fill_value=2)

                out_1 = paddle.static.nn.switch_case(
                    branch_index=index_1,
                    branch_fns={1: fn_1, 2: fn_2},
                    default=fn_3)

                out_2 = paddle.static.nn.switch_case(
                    branch_index=index_2,
                    branch_fns=[(1, fn_1), (2, fn_2)],
                    default=fn_3)

                # Argument default is None and no index matches. fn；,,_3 will be called because of the max index 7.
                out_3 = paddle.static.nn.switch_case(
                    branch_index=index_2,
                    branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)])

                exe = paddle.static.Executor(paddle.CPUPlace())
                res_1, res_2, res_3 = exe.run(main_program, fetch_list=[out_1, out_2, out_3])
                print(res_1)  # [[1. 1.]]
                print(res_2)  # [[2 2] [2 2]]
                print(res_3)  # [3 3 3]
    '''
    helper = LayerHelper('switch_case', **locals())

    def _check_args(branch_index, branch_fns, default):

        check_variable_and_dtype(
            branch_index,
            'branch_index',
            ['uint8', 'int32', 'int64'],
            'static.nn.switch_case',
        )

        if convert_dtype(branch_index.dtype) != "int64":
            branch_index = paddle.cast(branch_index, "int64")

        check_type(branch_fns, 'branch_fns', (list, tuple, dict), 'switch_case')

        branch_fns = (
            branch_fns.items() if isinstance(branch_fns, dict) else branch_fns
        )

        branch_fns = (
            list(enumerate(branch_fns))
            if all(callable(fn) for fn in branch_fns)
            else branch_fns
        )

        keys_of_fns = []
        for index_fn_pair in branch_fns:
            if not isinstance(index_fn_pair, tuple):
                raise TypeError(
                    _error_message(
                        "The elements' type",
                        "branch_fns",
                        "switch_case",
                        tuple,
                        type(branch_fns),
                    )
                )

            if len(index_fn_pair) != 2:
                raise TypeError(
                    _error_message(
                        "The tuple's size",
                        "branch_fns",
                        "switch_case",
                        "2",
                        str(len(index_fn_pair)) + "-tuple",
                    )
                )

            key, fn = index_fn_pair

            if not isinstance(key, int):
                raise TypeError(
                    _error_message(
                        "The key's type",
                        "branch_fns",
                        "switch_case",
                        int,
                        type(key),
                    )
                )

            if key in keys_of_fns:
                raise ValueError(
                    "The key in 'branch_fns' must be unique, but '{}' appears more than once.".format(
                        key
                    )
                )
            else:
                keys_of_fns.append(key)

            if not callable(fn):
                raise TypeError(
                    _error_message(
                        "The type of function for key {}".format(key),
                        "branch_fns",
                        "switch_case",
                        "callable",
                        type(fn),
                    )
                )

        if default is None:
            default = sorted(branch_fns)[-1][1]
            branch_fns = sorted(branch_fns)[:-1]
        elif not callable(default):
            raise TypeError("The default in Op(case) must be callable.")

        pred_fn_pairs = []
        for index, fn in branch_fns:
            new_index = paddle.full(shape=[1], dtype="int64", fill_value=index)
            pred = paddle.equal(branch_index, new_index)
            pred_fn_pairs.append((pred, fn))

        return pred_fn_pairs, default

    pred_fn_pairs, default = _check_args(branch_index, branch_fns, default)
    false_fn = default
    for pred, true_fn in pred_fn_pairs:
        false_fn = partial(cond, pred=pred, true_fn=true_fn, false_fn=false_fn)

    final_fn = false_fn
    return final_fn()