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
import ps_pb2 as pslib
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()

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

    def set_filelist(self, filelist):
        self.proto_desc.filelist.extend(filelist)

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

    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)


class DistMultiTrainer(TrainerDesc):
    def __init__(self, dataset=None, worker='Downpour', fleet_desc=None):
        super(DistMultiTrainer, self).__init__()
        if worker == "Downpour":
            self.proto_desc.device_worker_name = worker + "Worker"
            self.proto_desc.class_name = "DistMultiTrainer"
            self.proto_desc.data_feed.CopyFrom(dataset)
            downpour = self.proto_desc.downpour_param.add()
            # sparse table should specify:
            sparse_table = downpour.sparse_table.add()
            sparse_table.table_id = \
                         fleet_desc.trainer_param.sparse_table.table_id
            sparse_table.sparse_key_name.CopyFrom(fleet_desc.trainer_param()
                                                  .sparse_table().slot_key())
            sparse_table.sparse_value_name.CopyFrom(fleet_desc.trainer_param(
            ).sparse_table().slot_value())
            sparse_table.sparse_grad_name.CopyFrom(fleet_desc.trainer_param(
            ).sparse_table().slot_gradient())
            sparse_table.emb_dim = fleet_desc.server_param.downpour_server_param.downpour_table_param.accessor.fea_dim - 2
            sparse_table.fea_dim = downpour.emb_dim + 2
            sparse_table.label_var_name = "click"

            # dense table should specify:
            dense_table = downpour.dense_table.add()
            dense_table.table_id = \
                        fleet_desc.trainer_param.dense_table.table_id
            # dense_value_name
            dense_table.dense_value_name.CopyFrom(fleet_desc.trainer_param(
            ).dense_table().dense_variable_name)
            # dense_grad_name
            dense_table.dense_grad_name.CopyFrom(fleet_desc.trainer_param(
            ).dense_table().dense_gradient_name)
            downpour.skipped_ops.extend(fleet_desc.trainer_param.skip_op)
            print(str(self.proto_desc))
        else:
            raise ValueError('ValueError: DeviceWorker %s '
                             'is not supported in DistMultiTrainer' % worker)