fleet_base.py 34.9 KB
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#   Copyright (c) 2020 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
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import copy
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import warnings
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import paddle
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from paddle.fluid.framework import dygraph_only
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from paddle.fluid import compiler
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from .role_maker import UserDefinedRoleMaker, PaddleCloudRoleMaker, RoleMakerBase
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from .strategy_compiler import StrategyCompiler
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from .distributed_strategy import DistributedStrategy
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from .meta_optimizer_factory import MetaOptimizerFactory
from .runtime_factory import RuntimeFactory
from .util_factory import UtilFactory
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from paddle.fluid.wrapped_decorator import wrap_decorator
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from paddle.fluid.dygraph import parallel_helper
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def _inited_runtime_handler_(func):
    def __impl__(*args, **kwargs):
        cls = args[0]

        if cls._runtime_handle is None:
            raise ValueError("Fleet can not find suitable runtime handler")

        return func(*args, **kwargs)

    return __impl__


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def _is_non_distributed_check_(func):
    def __impl__(*args, **kwargs):
        cls = args[0]

        if cls._role_maker is not None and cls._role_maker._is_non_distributed(
        ) is True:
            warnings.warn(
                "%s() function doesn't work when use non_distributed fleet." %
                (func.__name__))
            return

        return func(*args, **kwargs)

    return __impl__


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inited_runtime_handler = wrap_decorator(_inited_runtime_handler_)
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is_non_distributed_check = wrap_decorator(_is_non_distributed_check_)
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class Fleet(object):
    """
    Unified API for distributed training of PaddlePaddle
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    Please reference the https://github.com/PaddlePaddle/FleetX for details
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    Returns:
        Fleet: A Fleet instance

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    Example for collective training:
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        .. code-block:: python

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            import paddle.distributed.fleet as fleet
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            fleet.init(is_collective=True)

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            strategy = fleet.DistributedStrategy()
            optimizer = paddle.optimizer.SGD(learning_rate=0.001)
            optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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            # do distributed training


    Example for parameter server training:

        .. code-block:: python

            import paddle.distributed.fleet as fleet

            fleet.init()

            strategy = fleet.DistributedStrategy()
            optimizer = paddle.optimizer.SGD(learning_rate=0.001)
            optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)

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            if fleet.is_first_worker():
                print("this is first worker")
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            print("current node index: {}".format(fleet.worker_index()))
            print("total number of worker num: {}".format(fleet.worker_num()))
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            if fleet.is_worker():
                print("this is worker")
            print("worker endpoints: {}".format(fleet.worker_endpoints(to_string=True)))
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            print("server num: {}".format(fleet.server_num()))
            print("server endpoints: {}".format(fleet.server_endpoints(to_string=True)))
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            if fleet.is_server():
                print("this is server")
            fleet.stop_worker()
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    """

    def __init__(self):
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        self._role_maker = None
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        self.strategy_compiler = None
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        self._is_collective = False
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        self._runtime_handle = None
        self._util = None
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    def init(self, role_maker=None, is_collective=False):
        """
        Initialize role_maker in Fleet.

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        This function is responsible for the distributed architecture
        what you want to run your code behind.

        Args:
            role_maker (RoleMakerBase, optional): A ``RoleMakerBase`` containing the configuration
                of environment variables related to distributed training.If you did not initialize 
                the rolemaker by yourself, it will be automatically initialized to PaddleRoleMaker.
                The default value is None.
            is_collective (Boolean, optional): A ``Boolean`` variable determines whether the program 
                runs on the CPU or GPU. False means set distributed training using CPU, and True means
                GPU.The default value is False.The default value is False.
        Returns:
            None

        Examples1:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

        Examples2:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init(is_collective=True)

        Examples3:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                role = fleet.PaddleCloudRoleMaker
                fleet.init(role)
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        """
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        if role_maker is None:
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            if isinstance(is_collective, bool):
                self._is_collective = is_collective
                self._role_maker = PaddleCloudRoleMaker(
                    is_collective=self._is_collective)
            else:
                raise ValueError(
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                    "`is_collective` should be instance of `bool`, but got {}".
                    format(type(is_collective)))
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        else:
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            if isinstance(role_maker, RoleMakerBase):
                self._role_maker = role_maker
            else:
                raise ValueError(
                    "`role_maker` should be subclass of `RoleMakerBase`, but got {}".
                    format(type(role_maker)))
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        self._role_maker._generate_role()
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        self.strategy_compiler = StrategyCompiler()
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        if paddle.fluid.framework.in_dygraph_mode():
            if parallel_helper._is_parallel_ctx_initialized():
                warnings.warn(
                    "The dygraph parallel environment has been initialized.")
            else:
                paddle.distributed.init_parallel_env()
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    def is_first_worker(self):
        """
        Check whether the node is the first instance of worker.

        Returns:
            bool: True if this is the first node of worker,
                  False if not.
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        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.is_first_worker()

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        """
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        return self._role_maker._is_first_worker()
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    def worker_index(self):
        """
        Get current worker index.

        Returns:
            int: node id
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        Examples:

            .. code-block:: python
                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.worker_index()

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        """
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        return self._role_maker._worker_index()
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    def worker_num(self):
        """
        Get current total worker number.

        Returns:
            int: worker numbers
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        Examples:
            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.worker_num()

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        """
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        return self._role_maker._worker_num()
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    def is_worker(self):
        """
        Check whether the node is an instance of worker.

        Returns:
            bool: True if this is a node of worker,
                  False if not.
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        Examples:
            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.is_worker()

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        """
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        return self._role_maker._is_worker()
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    def worker_endpoints(self, to_string=False):
        """
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        Get current worker endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].
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        Returns:
            list/string: server endpoints
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        Examples:
            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.worker_endpoints()

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        """
        if to_string:
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            return ",".join(self._role_maker._get_trainer_endpoints())
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        else:
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            return self._role_maker._get_trainer_endpoints()
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    def server_num(self):
        """
        Get current total worker number.

        Returns:
            int: server number
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        Examples:
            .. code-block:: python
            import paddle.distributed.fleet as fleet
            fleet.init()
            fleet.server_num()
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        """
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        return len(self._role_maker._get_pserver_endpoints())
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    def server_index(self):
        """
        Get current server index.

        Returns:
            int: node id
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        Examples:
            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.server_index()

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        """
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        return self._role_maker._server_index()
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    def server_endpoints(self, to_string=False):
        """
        Get current server endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].

        Returns:
            list/string: server endpoints
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        Examples:
            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.server_endpoints()

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        """
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        if to_string:
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            return ",".join(self._role_maker._get_pserver_endpoints())
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        else:
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            return self._role_maker._get_pserver_endpoints()
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    def is_server(self):
        """
        Check whether the node is an instance of server.

        Returns:
            bool: True if this is a node of server,
                  False if not.
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        Examples:

            .. code-block:: python
                import paddle.distributed.fleet as fleet
                fleet.init()
                fleet.is_server()

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        """
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        return self._role_maker._is_server(
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        ) or self._role_maker._is_heter_worker()
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    def set_util(self, util):
        self._util = util

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    def util(self):
        """
        Utility functions that can be used under certain runtime
        return util
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        Returns:
            UtilBase: instance of UtilBase, can use distributed ops/tools easily.

        Examples:

            .. code-block:: python
                import paddle.distributed.fleet as fleet
                fleet.init()
                util = fleet.util
                files = ["1.log", "2.log", "3.log", "4.log"]
                files = util.get_file_shard()

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        """
        return self._util

    def barrier_worker(self):
        """
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        barrier all workers

        Returns:
            None
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        """
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        self._role_maker._barrier("worker")
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    @is_non_distributed_check
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    @inited_runtime_handler
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    def init_worker(self):
        """
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        initialize `Communicator` for parameter server training.


        Returns:
            None

        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                fleet.init_worker()

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        """
        self._runtime_handle._init_worker()

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    @is_non_distributed_check
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    @inited_runtime_handler
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    def init_server(self, *args, **kwargs):
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        """
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        init_server executor to initialize startup program,
        if the `args` is not empty, it will run load_persistables for increment training.


        Returns:
            None

        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                fleet.init_server()

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        """
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        self._runtime_handle._init_server(*args, **kwargs)
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    @is_non_distributed_check
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    @inited_runtime_handler
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    def run_server(self):
        """
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        run server will run pserver main program with executor.

        Returns:
            None

        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                if fleet.is_server():
                    fleet.init_server()

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        """
        self._runtime_handle._run_server()

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    @is_non_distributed_check
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    @inited_runtime_handler
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    def stop_worker(self):
        """
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        stop `Communicator` and give training complete notice to parameter server.

        Returns:
            None

        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                fleet.init_server()

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        """
        self._runtime_handle._stop_worker()

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    def save_inference_model(self,
                             executor,
                             dirname,
                             feeded_var_names,
                             target_vars,
                             main_program=None,
                             export_for_deployment=True):
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        """
        save inference model for inference.

        Returns:
            None

        Examples:

            .. code-block:: python

                import paddle.distributed.fleet as fleet
                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                fleet.init_server()

        """

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        self._runtime_handle._save_inference_model(
            executor, dirname, feeded_var_names, target_vars, main_program,
            export_for_deployment)

    def save_persistables(self, executor, dirname, main_program=None):
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        """

        saves all persistable variables from :code:`main_program` to
        the folder :code:`dirname`. You can refer to

        The :code:`dirname` is used to specify the folder where persistable variables
        are going to be saved. If you would like to save variables in separate
        files, set :code:`filename` None.

        Args:
            executor(Executor): The executor to run for saving persistable variables.
                                You can refer to :ref:`api_guide_executor_en` for
                                more details.

            dirname(str, optional): The saving directory path.
                                When you need to save the parameter to the memory, set it to None.
            main_program(Program, optional): The program whose persistbale variables will
                                             be saved. Default: None.


        Returns:
            None

        Examples:

            .. code-block:: text

                import paddle.distributed.fleet as fleet
                import paddle.fluid as fluid

                fleet.init()

                # build net
                # fleet.distributed_optimizer(...)

                exe = fluid.Executor(fluid.CPUPlace())
                fleet.save_persistables(exe, "dirname", fluid.default_main_program())

        """

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        self._runtime_handle._save_persistables(executor, dirname, main_program)

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    def distributed_optimizer(self, optimizer, strategy=None):
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        """
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        Optimizer for distributed training.

        For the distributed training, this method would rebuild a new instance of DistributedOptimizer.
        Which has basic Optimizer function and special features for distributed training.

        Args:
            optimizer(Optimizer): The executor to run for init server.
            strategy(DistributedStrategy): Extra properties for distributed optimizer.

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        Returns:
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            Fleet: instance of fleet.
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        Examples:
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            .. code-block:: python
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                import paddle.distributed.fleet as fleet
                role = fleet.role_maker.PaddleCloudRoleMaker(is_collective=True)
                fleet.init(role)
                strategy = fleet.DistributedStrategy()
                optimizer = paddle.optimizer.SGD(learning_rate=0.001)
                optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)

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        """
        self.user_defined_optimizer = optimizer
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        if paddle.fluid.framework.in_dygraph_mode():
            return self

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        if strategy == None:
            strategy = DistributedStrategy()
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        self.user_defined_strategy = strategy
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        self.valid_strategy = None
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        return self

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    @dygraph_only
    def distributed_model(self, model):
        """
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        Return distributed data parallel model (Only work in dygraph mode)

        Args:
            model (Layer): the user-defind model which inherits Layer.

        Returns:
            distributed data parallel model which inherits Layer.
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        Examples:
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            .. code-block:: python

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                import paddle
                import paddle.nn as nn
                from paddle.distributed import fleet

                class LinearNet(nn.Layer):
                    def __init__(self):
                        super(LinearNet, self).__init__()
                        self._linear1 = nn.Linear(10, 10)
                        self._linear2 = nn.Linear(10, 1)
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                    def forward(self, x):
                        return self._linear2(self._linear1(x))
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                # 1. enable dynamic mode
                paddle.disable_static()

                # 2. initialize fleet environment
                fleet.init(is_collective=True)

                # 3. create layer & optimizer
                layer = LinearNet()
                loss_fn = nn.MSELoss()
                adam = paddle.optimizer.Adam(
                    learning_rate=0.001, parameters=layer.parameters())

                # 4. get data_parallel model using fleet
                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)

                # 5. run layer
                inputs = paddle.randn([10, 10], 'float32')
                outputs = dp_layer(inputs)
                labels = paddle.randn([10, 1], 'float32')
                loss = loss_fn(outputs, labels)

                print("loss:", loss.numpy())

                loss = dp_layer.scale_loss(loss)
                loss.backward()
                dp_layer.apply_collective_grads()

                adam.step()
                adam.clear_grad()

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        """
        assert model is not None
        self.model = paddle.DataParallel(model)
        return self.model

    @dygraph_only
    def state_dict(self):
        """
        Get state dict information from optimizer.
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        (Only work in dygraph mode)
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        Returns: 
            state_dict(dict) : dict contains all the Tensor used by optimizer

        Examples:
            .. code-block:: python

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                import numpy as np
                import paddle
                from paddle.distributed import fleet

                paddle.disable_static()
                fleet.init(is_collective=True)
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                value = np.arange(26).reshape(2, 13).astype("float32")
                a = paddle.fluid.dygraph.to_variable(value)
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                layer = paddle.nn.Linear(13, 5)
                adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
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                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)
                state_dict = adam.state_dict()
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        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.state_dict()

    @dygraph_only
    def set_state_dict(self, state_dict):
        """
        Load optimizer state dict.
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        (Only work in dygraph mode)
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        Args: 
            state_dict(dict) : Dict contains all the Tensor needed by optimizer

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

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                import numpy as np
                import paddle
                from paddle.distributed import fleet
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                paddle.disable_static()
                fleet.init(is_collective=True)

                value = np.arange(26).reshape(2, 13).astype("float32")
                a = paddle.fluid.dygraph.to_variable(value)
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                layer = paddle.nn.Linear(13, 5)
                adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
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                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)
                state_dict = adam.state_dict()
                paddle.framework.save(state_dict, "paddle_dy")
                para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
                adam.set_state_dict(opti_state_dict)
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        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.set_state_dict(state_dict)

    @dygraph_only
    def set_lr(self, value):
        """
        Set the value of the learning rate manually in the optimizer. 
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        (Only work in dygraph mode)
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        Args:
            value (float|Tensor): the value of learning rate

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

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                import numpy as np
                import paddle
                from paddle.distributed import fleet
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                paddle.disable_static()
                fleet.init(is_collective=True)
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                value = np.arange(26).reshape(2, 13).astype("float32")
                a = paddle.fluid.dygraph.to_variable(value)
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                layer = paddle.nn.Linear(13, 5)
                adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
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                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)

                lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
                for i in range(5):
                    adam.set_lr(lr_list[i])
                    lr = adam.get_lr()
                    print("current lr is {}".format(lr))
                # Print:
                #    current lr is 0.2
                #    current lr is 0.3
                #    current lr is 0.4
                #    current lr is 0.5
                #    current lr is 0.6
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        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.set_lr(value)

    @dygraph_only
    def get_lr(self):
        """
        Get current step learning rate.
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        (Only work in dygraph mode)
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        Returns:
            float: The learning rate of the current step.

        Examples:
            .. code-block:: python

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                import numpy as np
                import paddle
                from paddle.distributed import fleet

                paddle.disable_static()
                fleet.init(is_collective=True)
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                value = np.arange(26).reshape(2, 13).astype("float32")
                a = paddle.fluid.dygraph.to_variable(value)
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                layer = paddle.nn.Linear(13, 5)
                adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
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                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)
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                lr = adam.get_lr()
                print(lr) # 0.01
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        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.get_lr()

    @dygraph_only
    def step(self):
        """
        Execute the optimizer once.
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        (Only work in dygraph mode)
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        Returns:
            None
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        Examples:
            .. code-block:: python

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                import paddle
                import paddle.nn as nn
                from paddle.distributed import fleet
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                class LinearNet(nn.Layer):
                    def __init__(self):
                        super(LinearNet, self).__init__()
                        self._linear1 = nn.Linear(10, 10)
                        self._linear2 = nn.Linear(10, 1)
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                    def forward(self, x):
                        return self._linear2(self._linear1(x))
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                # 1. enable dynamic mode
                paddle.disable_static()

                # 2. initialize fleet environment
                fleet.init(is_collective=True)

                # 3. create layer & optimizer
                layer = LinearNet()
                loss_fn = nn.MSELoss()
                adam = paddle.optimizer.Adam(
                    learning_rate=0.001, parameters=layer.parameters())

                # 4. get data_parallel model using fleet
                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)

                # 5. run layer
                inputs = paddle.randn([10, 10], 'float32')
                outputs = dp_layer(inputs)
                labels = paddle.randn([10, 1], 'float32')
                loss = loss_fn(outputs, labels)

                print("loss:", loss.numpy())

                loss = dp_layer.scale_loss(loss)
                loss.backward()
                dp_layer.apply_collective_grads()

                adam.step()
                adam.clear_grad()


        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.step()

    @dygraph_only
    def clear_grad(self):
        """
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        Clear the gradients of all optimized parameters for model.
        (Only work in dygraph mode)
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        Returns: 
            None
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        Examples:
            .. code-block:: python

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                import paddle
                import paddle.nn as nn
                from paddle.distributed import fleet
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                class LinearNet(nn.Layer):
                    def __init__(self):
                        super(LinearNet, self).__init__()
                        self._linear1 = nn.Linear(10, 10)
                        self._linear2 = nn.Linear(10, 1)
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                    def forward(self, x):
                        return self._linear2(self._linear1(x))
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                # 1. enable dynamic mode
                paddle.disable_static()

                # 2. initialize fleet environment
                fleet.init(is_collective=True)

                # 3. create layer & optimizer
                layer = LinearNet()
                loss_fn = nn.MSELoss()
                adam = paddle.optimizer.Adam(
                    learning_rate=0.001, parameters=layer.parameters())

                # 4. get data_parallel model using fleet
                adam = fleet.distributed_optimizer(adam)
                dp_layer = fleet.distributed_model(layer)

                # 5. run layer
                inputs = paddle.randn([10, 10], 'float32')
                outputs = dp_layer(inputs)
                labels = paddle.randn([10, 1], 'float32')
                loss = loss_fn(outputs, labels)

                print("loss:", loss.numpy())

                loss = dp_layer.scale_loss(loss)
                loss.backward()
                dp_layer.apply_collective_grads()

                adam.step()
                adam.clear_grad()

        """
        # imitate target optimizer retrieval
        return self.user_defined_optimizer.clear_grad()

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    def minimize(self,
                 loss,
                 startup_program=None,
                 parameter_list=None,
                 no_grad_set=None):
        """
        Add distributed operations to minimize ``loss`` by updating ``parameter_list``.

        Args:
            loss (Variable): A ``Variable`` containing the value to minimize.
            startup_program (Program, optional): :ref:`api_fluid_Program` for
                initializing parameters in ``parameter_list``. The default value
                is None, at this time :ref:`api_fluid_default_startup_program` will be used.
            parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
                to minimize ``loss``. The default value is None, at this time all parameters
                will be updated.
            no_grad_set (set, optional): Set of ``Variable``  or ``Variable.name`` that don't need
                to be updated. The default value is None.

        Returns:
            tuple: tuple (optimize_ops, params_grads), A list of operators appended
            by minimize and a list of (param, grad) variable pairs, param is
            ``Parameter``, grad is the gradient value corresponding to the parameter.
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            The returned tuple can be passed to ``fetch_list`` in ``Executor.run()`` to
            indicate program pruning. If so, the program will be pruned by ``feed`` and
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            ``fetch_list`` before run, see details in ``Executor``.

        Examples:
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            .. code-block:: python
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                import paddle
                import paddle.distributed.fleet as fleet
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                fc_1 = paddle.fluid.layers.fc(input=input_x, size=hid_dim, act='tanh')
                fc_2 = paddle.fluid.layers.fc(input=fc_1, size=hid_dim, act='tanh')
                prediction = paddle.fluid.layers.fc(input=[fc_2], size=label_dim, act='softmax')
                cost = paddle.fluid.layers.cross_entropy(input=prediction, label=input_y)
                avg_cost = paddle.fluid.layers.mean(x=cost)

                role = fleet.role_maker.PaddleCloudRoleMaker(is_collective=True)
                fleet.init(role)
                strategy = fleet.DistributedStrategy()
                optimizer = paddle.optimizer.SGD(learning_rate=0.001)
                optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
                optimizer.minimize(avg_cost)
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                # for more examples, please reference https://github.com/PaddlePaddle/FleetX
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        """
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        if paddle.fluid.framework.in_dygraph_mode():
            # imitate target optimizer retrieval
            target_opt = self.user_defined_optimizer
            return target_opt.minimize(loss)

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        context = {}
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        # cache original feed forward program
        self.origin_main_program = loss.block.program
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        context["origin_main_program"] = self.origin_main_program
        context["loss"] = loss
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        if startup_program == None:
            self.origin_startup_program = \
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                paddle.static.default_startup_program().clone(for_test=False)
            startup_program = paddle.static.default_startup_program()
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        else:
            self.origin_startup_program = \
                startup_program.clone(for_test=False)
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        context["origin_startup_program"] = startup_program
        context["role_maker"] = self._role_maker
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        # compile time
        distributed_optimizer_list = \
            MetaOptimizerFactory()._get_valid_meta_optimizers(
                self.user_defined_optimizer)
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        context["user_defined_strategy"] = copy.copy(self.user_defined_strategy)

        # trigger the auto-parallel in very strict condition
        # strategy = DistributedStrategy()
        # strategy.auto = True
        # optimizer = paddle.optimizer.SGD(learning_rate=0.1)
        # optimizer = fleet.distributed_optimizer(optimizer, strategy)
        if self.user_defined_strategy._is_strict_auto():
            # turn on all the strategy for each optimizer
            for opt in distributed_optimizer_list:
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                opt._enable_strategy(self.user_defined_strategy, context)
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        valid_optimizer_list = []
        valid_graph_optimizer_list = []
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        can_not_apply_optimizer_list = []
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        # recall meta optimizers for ranking
        for opt in distributed_optimizer_list:
            opt._set_basic_info(loss, self._role_maker,
                                self.user_defined_optimizer,
                                self.user_defined_strategy)
            if opt._can_apply() and not opt._is_graph_out():
                valid_optimizer_list.append(opt)
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            elif opt._can_apply() and opt._is_graph_out():
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                valid_graph_optimizer_list.append(opt)
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            else:
                can_not_apply_optimizer_list.append(opt)
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        # combine recalled meta optimizers to be a valid meta optimizer
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        meta_optimizer, graph_optimizer = \
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            self.strategy_compiler.generate_optimizer(
                loss, self._role_maker, self.user_defined_optimizer,
                self.user_defined_strategy, valid_optimizer_list,
                valid_graph_optimizer_list)
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        valid_strategy = self.strategy_compiler._get_valid_strategy(
            self.user_defined_strategy, can_not_apply_optimizer_list)
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        context["valid_strategy"] = valid_strategy

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        self.valid_strategy = valid_strategy
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        self.valid_strategy._enable_env()
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        optimize_ops = []
        params_grads = []
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        if self._role_maker._is_non_distributed() and not self._is_collective:
            if self._runtime_handle is None:
                self._runtime_handle = RuntimeFactory()._create_runtime(context)

            compiled_program = compiler.CompiledProgram(
                self.origin_main_program).with_data_parallel(
                    loss_name=loss.name, share_vars_from=None)
            loss.block.program._graph = compiled_program
            return self.user_defined_optimizer.minimize(
                loss,
                startup_program=startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set)

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        if meta_optimizer:
            optimize_ops, params_grads = meta_optimizer.minimize(
                loss,
                startup_program=startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set)
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            default_program = paddle.static.default_main_program()
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            if id(default_program) != id(loss.block.program):
                paddle.fluid.framework.switch_main_program(loss.block.program)

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        else:
            optimize_ops, params_grads = self.user_defined_optimizer.minimize(
                loss,
                startup_program=startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set)
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        context["program_optimize_ops"] = optimize_ops
        context["program_params_grads"] = params_grads
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        if self.user_defined_strategy.zero:
            graph_optimizer = None

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        if graph_optimizer:
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            optimize_ops, params_grads = graph_optimizer.minimize(
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                loss,
                startup_program=startup_program,
                parameter_list=parameter_list,
                no_grad_set=no_grad_set)
            # since we do not encourage users to use graph operations
            # if a graph optimizer takes effect, mostly
            # optimizers_ops and params_grads are None
            # i.e. users can not modify current computation graph anymore
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            context["graph_optimize_ops"] = optimize_ops
            context["graph_optimize_grads"] = params_grads

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        if self._runtime_handle is None:
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            self._runtime_handle = RuntimeFactory()._create_runtime(context)
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        if self._util is None:
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            self._util = UtilFactory()._create_util(context)
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        return optimize_ops, params_grads