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

# NOTE: reduce removed in fuctools in python3
from functools import reduce


class Server:
    """
    A Server basic class.
    """

    def __init__(self):
        pass


class Worker:
    """
    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.server_thread_num = 12

    def add_sparse_table(
        self, table_id, learning_rate, slot_key_vars, slot_value_var
    ):
        r"""
        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
        """
        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.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):
        r"""
        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
        """
        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.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
        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)
        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
    ):
        r"""
        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
        """
        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):
        r"""
        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
        """
        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_