wehub-resource-sync
269 lines
8.2 KiB
Python
Executable File
269 lines
8.2 KiB
Python
Executable File
# 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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# Copyright(c) 2019 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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"""
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Communicator is used for async distribute training in distribute_transpiler mode.
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It's a wrapper of a cpp class Communicator and should be used inside fleet API.
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"""
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import paddle
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from paddle.distributed.ps.utils.public import DistributedMode
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from paddle.framework import core
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__all__ = []
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class Communicator:
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def __init__(self, mode, kwargs=None, envs=None):
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"""
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Communicator is used for async distribute training in distribute_transpiler mode.
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It's a wrapper of a cpp class Communicator and should be used inside fleet API.
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Args:
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program(Program): the trainers program after transpile of distribute_transpiler.
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It's used by communicator to extract the information to do communication.
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Returns:
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None
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> prog = paddle.static.Program()
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>>> comm = paddle.distributed.communicator.Communicator(prog)
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>>> comm.start()
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>>> comm.stop()
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"""
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# set all recv op to not_run mode
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if kwargs is None:
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if envs is None:
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envs = {}
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else:
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if mode == DistributedMode.SYNC:
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envs["pserver_endpoints"] = ','.join(
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kwargs["pserver_endpoints"]
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)
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envs["trainers"] = str(kwargs["trainers"])
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envs["trainer_id"] = str(kwargs["trainer_id"])
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envs["need_global_step"] = str(kwargs["need_global_step"])
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envs["barrier_table_id"] = str(kwargs["barrier_table_id"])
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mode_str = None
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if mode == DistributedMode.SYNC:
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mode_str = "SYNC"
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elif mode == DistributedMode.ASYNC:
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mode_str = "ASYNC"
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elif mode == DistributedMode.HALF_ASYNC:
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mode_str = "HALF_ASYNC"
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elif mode == DistributedMode.GEO:
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mode_str = "GEO"
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self.mode = mode_str
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self.envs = envs
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self.communicator_ = None
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self.send_ctx_ = None
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self.recv_ctx_ = None
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def init_with_ctx(
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self, send_ctx, recv_ctx, proto_txt, unit64_hosts, scope=None
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):
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if scope is None:
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scope = paddle.static.global_scope()
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self.communicator_ = core.DistCommunicator(
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self.mode,
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proto_txt,
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unit64_hosts,
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send_ctx,
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recv_ctx,
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scope,
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self.envs,
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)
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self.send_ctx_ = send_ctx
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self.recv_ctx_ = recv_ctx
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def create_client_to_client_connection(
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self,
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pserver_timeout_ms=500000,
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pserver_connect_timeout_ms=10000,
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max_retry=3,
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):
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self.communicator_.create_client_to_client_connection(
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pserver_timeout_ms, pserver_connect_timeout_ms, max_retry
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)
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def get_client_info(self):
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return self.communicator_.get_client_info()
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def set_clients(self, host_list):
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self.communicator_.set_clients(host_list)
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def start(self):
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"""
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Start communicator. Should call before training process.
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Returns:
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None
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> prog = paddle.static.Program()
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>>> comm = paddle.distributed.communicator.Communicator(prog)
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>>> comm.start()
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>>> comm.stop()
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"""
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if self.communicator_ is None:
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print('you must call init_with_ctx first to init comm before start')
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return
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self.communicator_.start()
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def stop(self):
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"""
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Stop communicator. Should call after training process.
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Returns:
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None
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> prog = paddle.static.Program()
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>>> comm = paddle.distributed.communicator.Communicator(prog)
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>>> comm.start()
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>>> comm.stop()
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"""
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if self.communicator_ is None:
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print('you must call init_with_ctx first to init comm before stop')
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return
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self.communicator_.stop()
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def is_running(self):
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"""
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Get communicator is running or stop.
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Returns:
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bool
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> prog = paddle.static.Program()
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>>> comm = paddle.distributed.communicator.Communicator(prog)
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>>> comm.is_running()
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"""
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if self.communicator_ is None:
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print('you must call init_with_ctx first to init comm before stop')
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return
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self.communicator_.is_running()
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def recv(self):
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self.communicator_.recv()
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def init_params(self, context):
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self.communicator_.init_params(context)
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def pull_dense(self, context):
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self.communicator_.pull_dense(context)
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def push_sparse_param(self, var_name, table_id=-1, scope=None):
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if scope is None:
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scope = paddle.static.global_scope()
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if not self.is_running():
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raise ValueError(
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"Communicator should init first. Using fleet.init_worker() before push_sparse_param()"
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)
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assert isinstance(var_name, str)
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assert isinstance(table_id, int)
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if table_id == -1:
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table_id = self.send_ctx_[var_name].table_id()
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self.communicator_.push_sparse_param(var_name, table_id, scope)
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class FLCommunicator(Communicator): # only for coordinator
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def __init__(self, ps_hosts, kwargs=None):
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mode = None
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super().__init__(mode, kwargs)
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send_ctx = {}
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dense_map = {}
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prototxt = ""
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self.mode = "WITH_COORDINATOR"
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self.init_with_ctx(send_ctx, dense_map, prototxt, ps_hosts)
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def start_coordinator(self, self_endpoint, trainer_endpoints):
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if self.communicator_ is not None:
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self.communicator_.start_coordinator(
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self_endpoint, trainer_endpoints
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)
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def save_fl_strategy(self, mp):
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if self.communicator_ is not None:
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self.communicator_.save_fl_strategy(mp)
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else:
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raise ValueError("self.communicator_ is null")
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def query_fl_clients_info(self):
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info_mp = {}
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if self.communicator_ is not None:
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info_mp = self.communicator_.query_fl_clients_info()
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return info_mp
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class LargeScaleKV:
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def __init__(self):
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self.scale_kv = core.LargeScaleKV()
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def save(self, varname, dirname):
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self.scale_kv.save(varname, dirname)
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def load(self, varname, dirname):
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self.scale_kv.load(varname, dirname)
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def size(self, varname):
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return self.scale_kv.size(varname)
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class HeterClient:
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def __init__(self, endpoint, previous_endpoint, trainer_id):
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self.heter_client_ = core.HeterClient(
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endpoint, previous_endpoint, trainer_id
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)
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def stop(self):
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self.heter_client_.stop()
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