node.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

import ps_pb2 as pslib


class Server(object):
    """
        A Server basic class.
    """

    def __init__(self):
        pass


class Worker(object):
    """
        A Worker basic class.
    """

    def __init__(self):
        pass


class DownpourServer(Server):
    """
        DownpourServer class is used to generate server program_desc
        Args:
            server: it is pslib.ServerParameter() 
        Examples:
            server = DownpourServer()
    """

    def __init__(self):
        self._server = pslib.ServerParameter()
        self._server.downpour_server_param.service_param.start_server_port = 0
        self._server.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
        self._server.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
        self._server.downpour_server_param.service_param.service_class = "DownpourPsService"
        self._server.downpour_server_param.service_param.start_server_port = 0
        self._server.downpour_server_param.service_param.server_thread_num = 12

    def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                         slot_value_var):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            slot_key_vars(string): slot key id 
            slot_value_var(string): slot key value after embedding
        Returns:
            return None 
        """
        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_SPARSE_TABLE:
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_SPARSE_TABLE, table.type))
        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        table.table_class = "DownpourSparseTable"
        table.type = pslib.PS_SPARSE_TABLE
        table.compress_in_save = True
        table.accessor.accessor_class = "DownpourFeatureValueAccessor"
        table.accessor.sparse_sgd_param.learning_rate = learning_rate
        table.accessor.sparse_sgd_param.initial_g2sum = 3
        table.accessor.sparse_sgd_param.initial_range = 1e-4
        table.accessor.sparse_sgd_param.weight_bounds.extend([-10, 10])

        table.accessor.embedx_dim = 8
        table.accessor.embedx_threshold = 5
        table.accessor.fea_dim = 11
        table.accessor.downpour_accessor_param.nonclk_coeff = 0.1
        table.accessor.downpour_accessor_param.click_coeff = 2
        table.accessor.downpour_accessor_param.base_threshold = 0.2
        table.accessor.downpour_accessor_param.delta_threshold = 0.15
        table.accessor.downpour_accessor_param.delta_keep_days = 31
        table.accessor.downpour_accessor_param.show_click_decay_rate = 0.999
        table.accessor.downpour_accessor_param.delete_threshold = 0.8

    def add_dense_table(self, table_id, learning_rate, param_var, grad_var):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            param_var(list): all dense param. it is a list.
            grad_var(list): all dense grad parm it is a list.
        Returns:
            return None 
        """
        fea_dim = 0
        for param in filter(lambda x: x.name.find("embedding") == -1,
                            param_var):
            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)

        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_DENSE_TABLE:
                    table.accessor.fea_dim = fea_dim
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_DENSE_TABLE, table.type))
        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        table.table_class = "DownpourDenseTable"
        table.type = pslib.PS_DENSE_TABLE
        table.compress_in_save = True
        table.accessor.accessor_class = "DownpourDenseValueAccessor"
        table.accessor.dense_sgd_param.name = "adam"
        table.accessor.dense_sgd_param.adam.learning_rate = learning_rate
        table.accessor.dense_sgd_param.adam.avg_decay_rate = 0.999993
        table.accessor.dense_sgd_param.adam.ada_decay_rate = 0.9999
        table.accessor.dense_sgd_param.adam.ada_epsilon = 1e-8
        table.accessor.dense_sgd_param.adam.mom_decay_rate = 0.99
        table.accessor.dense_sgd_param.naive.learning_rate = 0.0002
        table.accessor.fea_dim = fea_dim

    def add_data_norm_table(self, table_id, learning_rate, param_var, grad_var):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            param_var(list): all dense param. it is a list.
            grad_var(list): all dense grad parm it is a list.
        Returns:
            return None 
        """
        fea_dim = 0
        for param in filter(lambda x: x.name.find("embedding") == -1,
                            param_var):
            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)

        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_DENSE_TABLE:
                    table.accessor.fea_dim = fea_dim
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_DENSE_TABLE, table.type))
        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        table.table_class = "DownpourDenseTable"
        table.type = pslib.PS_DENSE_TABLE
        table.compress_in_save = True
        table.accessor.accessor_class = "DownpourDenseValueAccessor"
        table.accessor.dense_sgd_param.name = "summary"
        table.accessor.dense_sgd_param.summary.summary_decay_rate = 0.999999
        table.accessor.fea_dim = fea_dim

    def get_desc(self):
        """
        Return downpour server program_desc
        """
        return self._server


class DownpourWorker(Worker):
    """
        DownpourWorker class is used to generate worker program_desc
        Args:
            window (int): push params frequency
            worker: it is pslib.DownpourTrainerParameter 
        Examples:
            worker = DownpourWorker(1)
    """

    def __init__(self, window):
        self.window = window
        self._worker = pslib.DownpourTrainerParameter()

    def add_sparse_table(self, table_id, learning_rate, slot_key_vars,
                         slot_value_vars):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            slot_key_vars(string): slot key id 
            slot_value_var(string): slot key value after embedding
        Returns:
            return None 
        """
        for table in self._worker.sparse_table:
            if table.table_id == table_id:
                return
        table = self._worker.sparse_table.add()
        table.table_id = table_id
        table.slot_key.extend([var.name for var in slot_key_vars])
        table.slot_value.extend([var.name for var in slot_value_vars])
        table.slot_gradient.extend(
            [var.name + "@GRAD" for var in slot_value_vars])

    def add_dense_table(self, table_id, learning_rate, param_vars, grad_vars):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            param_var(list): all dense param. it is a list.
            grad_var(list): all dense grad parm it is a list.
        Returns:
            return None 
        """
        for table in self._worker.dense_table:
            if table.table_id == table_id:
                return
        table = self._worker.dense_table.add()
        table.table_id = table_id
        table.dense_variable_name.extend(
            filter(lambda x: x.find("embedding") == -1,
                   [p.name for p in param_vars]))
        table.dense_gradient_variable_name.extend(
            filter(lambda x: x.find("embedding") == -1,
                   [g.name for g in grad_vars]))

    def get_desc(self):
        """
        Return downpour worker program_desc
        """
        return self._worker