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# 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
"""
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):
"""
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):
"""
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):
"""
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_