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717 lines
22 KiB
717 lines
22 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import paddle
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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
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from .runtime_factory import RuntimeFactory
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from .util_factory import UtilFactory
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from paddle.fluid.wrapped_decorator import wrap_decorator
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def _inited_runtime_handler_(func):
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def __impl__(*args, **kwargs):
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cls = args[0]
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if cls._runtime_handle is None:
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raise ValueError("Fleet can not find suitable runtime handler")
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return func(*args, **kwargs)
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return __impl__
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inited_runtime_handler = wrap_decorator(_inited_runtime_handler_)
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class Fleet(object):
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"""
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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:
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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()
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optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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# do distributed training
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Example for parameter server training:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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strategy = fleet.DistributedStrategy()
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optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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if fleet.is_first_worker():
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print("this is first worker")
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print("current node index: {}".format(fleet.worker_index()))
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print("total number of worker num: {}".format(fleet.worker_num()))
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if fleet.is_worker():
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print("this is worker")
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print("worker endpoints: {}".format(fleet.worker_endpoints(to_string=True)))
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print("server num: {}".format(fleet.server_num()))
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print("server endpoints: {}".format(fleet.server_endpoints(to_string=True)))
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if fleet.is_server():
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print("this is server")
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fleet.stop_worker()
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"""
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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
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self._util = None
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def init(self, role_maker=None, is_collective=False):
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"""
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Initialize role_maker in Fleet.
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This function is responsible for the distributed architecture
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what you want to run your code behind.
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Args:
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role_maker (RoleMakerBase, optional): A ``RoleMakerBase`` containing the configuration
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of environment variables related to distributed training.If you did not initialize
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the rolemaker by yourself, it will be automatically initialized to PaddleRoleMaker.
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The default value is None.
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is_collective (Boolean, optional): A ``Boolean`` variable determines whether the program
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runs on the CPU or GPU. False means set distributed training using CPU, and True means
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GPU.The default value is False.The default value is False.
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Returns:
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None
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Examples1:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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Examples2:
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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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Examples3:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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role = fleet.PaddleCloudRoleMaker
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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):
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self._is_collective = is_collective
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self._role_maker = PaddleCloudRoleMaker(
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is_collective=self._is_collective)
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else:
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raise ValueError(
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"`is_collective` should be instance of `bool`, but got {}".
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format(type(is_collective)))
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else:
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if isinstance(role_maker, RoleMakerBase):
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self._role_maker = role_maker
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else:
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raise ValueError(
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"`role_maker` should be subclass of `RoleMakerBase`, but got {}".
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format(type(role_maker)))
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self.strategy_compiler = StrategyCompiler()
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return None
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def is_first_worker(self):
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"""
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Check whether the node is the first instance of worker.
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Returns:
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bool: True if this is the first node of worker,
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False if not.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Get current worker index.
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Returns:
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int: node id
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Get current total worker number.
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Returns:
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int: worker numbers
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Check whether the node is an instance of worker.
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Returns:
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bool: True if this is a node of worker,
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False if not.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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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"""
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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:
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list/string: server endpoints
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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fleet.worker_endpoints()
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"""
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'''
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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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'''
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return ["127.0.0.1:1001", "127.0.0.1:1002"]
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def server_num(self):
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"""
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Get current total worker number.
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Returns:
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int: server number
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Get current server index.
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Returns:
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int: node id
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Get current server endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].
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Returns:
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list/string: server endpoints
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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):
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"""
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Check whether the node is an instance of server.
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Returns:
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bool: True if this is a node of server,
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False if not.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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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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@property
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def util(self):
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"""
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Utility functions that can be used under certain runtime
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return util
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Returns:
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UtilBase: instance of UtilBase, can use distributed ops/tools easily.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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util = fleet.util
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files = ["1.log", "2.log", "3.log", "4.log"]
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files = util.get_file_shard()
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"""
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return self._util
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@util.setter
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def util(self, util):
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"""
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Set Utility functions for userd-defined runtime
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Returns:
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None
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"""
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self._util = util
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def barrier_worker(self):
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"""
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barrier all workers
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Returns:
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None
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"""
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self._role_maker.barrier_worker()
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@inited_runtime_handler
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def init_worker(self):
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"""
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initialize `Communicator` for parameter server training.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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fleet.init_worker()
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"""
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self._runtime_handle._init_worker()
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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,
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if the `args` is not empty, it will run load_persistables for increment training.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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fleet.init_server()
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"""
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self._runtime_handle._init_server(*args, **kwargs)
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@inited_runtime_handler
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def run_server(self):
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"""
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run server will run pserver main program with executor.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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if fleet.is_server():
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fleet.init_server()
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"""
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self._runtime_handle._run_server()
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@inited_runtime_handler
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def stop_worker(self):
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"""
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stop `Communicator` and give training complete notice to parameter server.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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fleet.init_server()
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"""
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self._runtime_handle._stop_worker()
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def save_inference_model(self,
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executor,
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dirname,
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feeded_var_names,
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target_vars,
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main_program=None,
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export_for_deployment=True):
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"""
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save inference model for inference.
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Returns:
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None
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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fleet.init_server()
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"""
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self._runtime_handle._save_inference_model(
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executor, dirname, feeded_var_names, target_vars, main_program,
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export_for_deployment)
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def save_persistables(self, executor, dirname, main_program=None):
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"""
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saves all persistable variables from :code:`main_program` to
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the folder :code:`dirname`. You can refer to
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The :code:`dirname` is used to specify the folder where persistable variables
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are going to be saved. If you would like to save variables in separate
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files, set :code:`filename` None.
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Args:
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executor(Executor): The executor to run for saving persistable variables.
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You can refer to :ref:`api_guide_executor_en` for
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more details.
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dirname(str, optional): The saving directory path.
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When you need to save the parameter to the memory, set it to None.
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main_program(Program, optional): The program whose persistbale variables will
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be saved. Default: None.
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Returns:
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None
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Examples:
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.. code-block:: text
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import paddle.distributed.fleet as fleet
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import paddle.fluid as fluid
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fleet.init()
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# build net
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# fleet.distributed_optimizer(...)
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exe = fluid.Executor(fluid.CPUPlace())
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fleet.save_persistables(exe, "dirname", fluid.default_main_program())
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"""
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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.
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For the distributed training, this method would rebuild a new instance of DistributedOptimizer.
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Which has basic Optimizer function and special features for distributed training.
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Args:
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optimizer(Optimizer): The executor to run for init server.
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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
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role = fleet.role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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strategy = fleet.DistributedStrategy()
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optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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"""
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self.user_defined_optimizer = optimizer
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if strategy == None:
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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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def minimize(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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"""
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Add distributed operations to minimize ``loss`` by updating ``parameter_list``.
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Args:
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loss (Variable): A ``Variable`` containing the value to minimize.
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startup_program (Program, optional): :ref:`api_fluid_Program` for
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initializing parameters in ``parameter_list``. The default value
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is None, at this time :ref:`api_fluid_default_startup_program` will be used.
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parameter_list (Iterable, optional): Iterable of ``Variable`` or ``Variable.name`` to update
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to minimize ``loss``. The default value is None, at this time all parameters
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will be updated.
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no_grad_set (set, optional): Set of ``Variable`` or ``Variable.name`` that don't need
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to be updated. The default value is None.
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Returns:
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tuple: tuple (optimize_ops, params_grads), A list of operators appended
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by minimize and a list of (param, grad) variable pairs, param is
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``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
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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``.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.distributed.fleet as fleet
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fc_1 = paddle.fluid.layers.fc(input=input_x, size=hid_dim, act='tanh')
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fc_2 = paddle.fluid.layers.fc(input=fc_1, size=hid_dim, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc_2], size=label_dim, act='softmax')
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cost = paddle.fluid.layers.cross_entropy(input=prediction, label=input_y)
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avg_cost = paddle.fluid.layers.mean(x=cost)
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role = fleet.role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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strategy = fleet.DistributedStrategy()
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optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
|
|
optimizer.minimize(avg_cost)
|
|
|
|
# for more examples, please reference https://github.com/PaddlePaddle/FleetX
|
|
|
|
"""
|
|
context = {}
|
|
# cache original feed forward program
|
|
self.origin_main_program = loss.block.program
|
|
context["origin_main_program"] = self.origin_main_program
|
|
context["loss"] = loss
|
|
if startup_program == None:
|
|
self.origin_startup_program = \
|
|
paddle.static.default_startup_program().clone(for_test=False)
|
|
startup_program = paddle.static.default_startup_program()
|
|
else:
|
|
self.origin_startup_program = \
|
|
startup_program.clone(for_test=False)
|
|
|
|
context["origin_startup_program"] = startup_program
|
|
context["role_maker"] = self._role_maker
|
|
|
|
# compile time
|
|
distributed_optimizer_list = \
|
|
MetaOptimizerFactory()._get_valid_meta_optimizers(
|
|
self.user_defined_optimizer)
|
|
|
|
valid_optimizer_list = []
|
|
valid_graph_optimizer_list = []
|
|
can_not_apply_optimizer_list = []
|
|
# 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)
|
|
elif opt._can_apply() and opt._is_graph_out():
|
|
valid_graph_optimizer_list.append(opt)
|
|
else:
|
|
can_not_apply_optimizer_list.append(opt)
|
|
# combine recalled meta optimizers to be a valid meta optimizer
|
|
meta_optimizer, graph_optimizer = \
|
|
self.strategy_compiler.generate_optimizer(
|
|
loss, self._role_maker, self.user_defined_optimizer,
|
|
self.user_defined_strategy, valid_optimizer_list,
|
|
valid_graph_optimizer_list)
|
|
|
|
valid_strategy = self.strategy_compiler._get_valid_strategy(
|
|
self.user_defined_strategy, can_not_apply_optimizer_list)
|
|
|
|
context["valid_strategy"] = valid_strategy
|
|
|
|
self.valid_strategy = valid_strategy
|
|
self.valid_strategy._enable_env()
|
|
|
|
optimize_ops = []
|
|
params_grads = []
|
|
|
|
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)
|
|
|
|
default_program = paddle.static.default_main_program()
|
|
|
|
if id(default_program) != id(loss.block.program):
|
|
paddle.fluid.framework.switch_main_program(loss.block.program)
|
|
|
|
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)
|
|
|
|
context["program_optimize_ops"] = optimize_ops
|
|
context["program_params_grads"] = params_grads
|
|
|
|
if graph_optimizer:
|
|
optimize_ops, params_grads = graph_optimizer.minimize(
|
|
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
|
|
context["graph_optimize_ops"] = optimize_ops
|
|
context["graph_optimize_grads"] = params_grads
|
|
|
|
if self._runtime_handle is None:
|
|
self._runtime_handle = RuntimeFactory()._create_runtime(context)
|
|
|
|
if self._util is None:
|
|
self._util = UtilFactory()._create_util(context)
|
|
|
|
return optimize_ops, params_grads
|