trainer_desc.py

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

from paddle.fluid.proto import trainer_desc_pb2
from distributed import ps_pb2 as ps_pb2
from device_worker import DeviceWorkerFactory
from google.protobuf import text_format

__all__ = ['TrainerDesc', 'MultiTrainer', 'DistMultiTrainer']


# can be initialized from train_desc, 
class TrainerDesc(object):
    def __init__(self):
        '''
        self.proto_desc = data_feed_pb2.DataFeedDesc()
        with open(proto_file, 'r') as f:
            text_format.Parse(f.read(), self.proto_desc)
        '''
        self.proto_desc = trainer_desc_pb2.TrainerDesc()
        import multiprocessing as mp
        # set default thread num == cpu count
        self.proto_desc.thread_num = mp.cpu_count()

    def set_thread(self, thread_num):
        self.proto_desc.thread_num = thread_num

    def set_filelist(self, filelist):
        self.proto_desc.filelist.extend(filelist)
        self.proto_desc.thread_num = min(
            len(filelist), self.proto_desc.thread_num)

    def set_data_feed(self, datafeed):
        self.proto_desc.data_desc.CopyFrom(datafeed.proto_desc)

    def gen_trainer_desc(self, dataset=None, fleet_desc=None, worker=None):
        pass

    def _desc(self):
        return text_format.MessageToString(self.proto_desc)


class MultiTrainer(TrainerDesc):
    def __init__(self, dataset=None, worker="Hogwild"):
        super(MultiTrainer, self).__init__()
        if worker == "Hogwild":
            self.proto_desc.device_worker_name = worker + "Worker"
            self.proto_desc.class_name = "MultiTrainer"
        else:
            raise ValueError('ValueError: DeviceWorker %s '
                             'is not supported in MultiTrainer' % worker)

    def gen_trainer_desc(self, dataset=None, fleet_desc=None, worker="Hogwild"):
        super(MultiTrainer, self).gen_trainer_desc(fleet_desc, worker)


class DistMultiTrainer(TrainerDesc):
    def __init__(self):
        super(DistMultiTrainer, self).__init__()
        pass

    def gen_trainer_desc(self, dataset=None, fleet_desc=None,
                         worker="Downpour"):
        super(DistMultiTrainer, self).gen_trainer_desc(fleet_desc, worker)
        self.proto_desc.class_name = "DistMultiTrainer"
        self.proto_desc.data_desc.CopyFrom(dataset.proto_desc)
        worker_builder = DeviceWorkerFactory()
        device_worker = worker_builder.create_device_worker("Downpour")
        device_worker.gen_worker_desc(self.proto_desc, fleet_desc)

    def set_program_config(self, fleet_desc, program_id):
        for program_config in fleet_desc.trainer_param.program_config:
            if program_config.program_id == program_id:
                pc = self.proto_desc.downpour_param.program_config.add()
                pc.program_id = program_config.program_id
                for i in program_config.push_sparse_table_id:
                    pc.push_sparse_table_id.extend([i])
                for i in program_config.push_dense_table_id:
                    pc.push_dense_table_id.extend([i])
                for i in program_config.pull_sparse_table_id:
                    pc.pull_sparse_table_id.extend([i])
                for i in program_config.pull_dense_table_id:
                    pc.pull_dense_table_id.extend([i])
                break