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framework.py
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framework.py
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# Copyright (c) 2020 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 utils.role_maker import FLSimRoleMaker
from clients import DataClient
from clients import SchedulerClient
from servers import DataServer
from servers import SchedulerServer
from multiprocessing import Process, Pool, Manager, Pipe, Lock
from utils.logger import logging
import pickle
import time
import numpy as np
import sys
import os
class SimulationFramework(object):
def __init__(self, role_maker):
self.data_client = None
self.scheduler_client = None
self.role_maker = role_maker
# we suppose currently we train homogeneous model
self.trainer = None
# for sampling users
self.sampler = None
# for update global weights
self.fl_optimizer = None
# for samping users to test
self.test_sampler = None
self.profile_file = open("profile", "w")
self.do_profile = True
# for data downloading
self.hdfs_configs = None
def set_hdfs_configs(self, configs):
self.hdfs_configs = configs
def set_trainer(self, trainer):
self.trainer = trainer
def set_sampler(self, sampler):
self.sampler = sampler
def set_test_sampler(self, sampler):
self.test_sampler = sampler
def set_fl_optimizer(self, optimizer):
self.fl_optimizer = optimizer
def is_scheduler(self):
return self.role_maker.is_global_scheduler()
def is_simulator(self):
return self.role_maker.is_simulator()
def run_scheduler_service(self):
if self.role_maker.is_global_scheduler():
self._run_global_scheduler()
def _barrier_simulators(self):
self.role_maker.barrier_simulator()
def _start_data_server(self, endpoint):
data_server = DataServer()
port = endpoint.split(":")[1]
data_server.start(endpoint=port)
def _run_global_scheduler(self):
scheduler_server = SchedulerServer()
endpoint = self.role_maker.get_global_scheduler_endpoint()
port = endpoint.split(":")[1]
scheduler_server.start(endpoint=port)
def _get_data_services(self):
data_server_endpoints = \
self.role_maker.get_local_data_server_endpoint()
data_server_pros = []
for i, ep in enumerate(data_server_endpoints):
p = Process(target=self._start_data_server, args=(ep, ))
data_server_pros.append(p)
return data_server_pros
def _profile(self, func, *args, **kwargs):
if self.do_profile:
start = time.time()
res = func(*args, **kwargs)
end = time.time()
self.profile_file.write("%s\t\t%f s\n" %
(func.__name__, end - start))
return res
else:
return func(*args, **kwargs)
def _run_sim(self, date, sim_num_everyday=1):
sim_idx = self.role_maker.simulator_idx()
sim_num = self.role_maker.simulator_num()
sim_all_trainer_run_time = 0
sim_read_praram_and_optimize = 0
for sim in range(sim_num_everyday):
logging.info("sim id: %d" % sim)
# sampler algorithm
user_info_dict = self._profile(
self.sampler.sample_user_list, self.scheduler_client, date,
sim_idx, len(self.data_client.stub_list), sim_num)
if self.do_profile:
print("sim_idx: ", sim_idx)
print("shard num: ", len(self.data_client.stub_list))
print("sim_num: ", sim_num)
print("user_info_dict: ", user_info_dict)
global_param_dict = self._profile(
self.scheduler_client.get_global_params)
processes = []
os.system("rm -rf _global_param")
os.system("mkdir _global_param")
start = time.time()
for idx, user in enumerate(user_info_dict):
arg_dict = {
"uid": str(user),
"date": date,
"data_endpoints":
self.role_maker.get_data_server_endpoints(),
"global_params": global_param_dict,
"user_param_names": self.trainer.get_user_param_names(),
"global_param_names":
self.trainer.get_global_param_names(),
"write_global_param_file":
"_global_param/process_%d" % idx,
}
p = Process(
target=self.trainer.train_one_user_func,
args=(arg_dict, self.trainer.trainer_config))
p.start()
processes.append(p)
if self.do_profile:
logging.info("wait processes to close")
for i, p in enumerate(processes):
processes[i].join()
end = time.time()
sim_all_trainer_run_time += (end - start)
start = time.time()
train_result = []
new_global_param_by_user = {}
training_sample_by_user = {}
for i, p in enumerate(processes):
param_dir = "_global_param/process_%d/" % i
with open(param_dir + "/_info", "r") as f:
user, train_sample_num = pickle.load(f)
param_dict = {}
for f_name in os.listdir(os.path.join(param_dir, "params")):
f_path = os.path.join(param_dir, "params", f_name)
if os.path.isdir(f_path): # layer
for layer_param in os.listdir(f_path):
layer_param_path = os.path.join(f_path,
layer_param)
with open(layer_param_path) as f:
param_dict["{}/{}".format(
f_name, layer_param)] = np.load(f)
else:
with open(f_path) as f:
param_dict[f_name] = np.load(f)
new_global_param_by_user[user] = param_dict
training_sample_by_user[user] = train_sample_num
self.fl_optimizer.update(training_sample_by_user,
new_global_param_by_user,
global_param_dict, self.scheduler_client)
end = time.time()
sim_read_praram_and_optimize += (end - start)
if self.do_profile:
self.profile_file.write("sim_all_trainer_run_time\t\t%f s\n" %
sim_all_trainer_run_time)
self.profile_file.write("sim_read_praram_and_optimize\t\t%f s\n" %
sim_read_praram_and_optimize)
logging.info("training done for date %s." % date)
def _test(self, date):
if self.trainer.infer_one_user_func is None:
pass
logging.info("doing test...")
if self.test_sampler is None:
logging.error("self.test_sampler should not be None when testing")
sim_idx = self.role_maker.simulator_idx()
sim_num = self.role_maker.simulator_num()
user_info_dict = self.test_sampler.sample_user_list(
self.scheduler_client,
date,
sim_idx,
len(self.data_client.stub_list),
sim_num, )
if self.do_profile:
print("test user info_dict: ", user_info_dict)
global_param_dict = self.scheduler_client.get_global_params()
def divide_chunks(l, n):
for i in range(0, len(l), n):
yield l[i:i + n]
# at most 50 process for testing
chunk_size = 50
# at most 100 uid for testing
max_test_uids = 100
uid_chunks = divide_chunks(user_info_dict.keys(), chunk_size)
os.system("rm -rf _test_result")
os.system("mkdir _test_result")
tested_uids = 0
for uids in uid_chunks:
if tested_uids >= max_test_uids:
break
processes = []
for user in uids:
arg_dict = {
"uid": str(user),
"date": date,
"data_endpoints":
self.role_maker.get_data_server_endpoints(),
"global_params": global_param_dict,
"user_param_names": self.trainer.get_user_param_names(),
"global_param_names":
self.trainer.get_global_param_names(),
"infer_result_dir": "_test_result/uid-%s" % user,
}
p = Process(
target=self.trainer.infer_one_user_func,
args=(arg_dict, self.trainer.trainer_config))
p.start()
processes.append(p)
if self.do_profile:
logging.info("wait test processes to close")
for i, p in enumerate(processes):
processes[i].join()
tested_uids += chunk_size
infer_results = []
# only support one test metric now
for uid in os.listdir("_test_result"):
with open("_test_result/" + uid + "/res", 'r') as f:
sample_cout, metric = f.readlines()[0].strip('\n').split('\t')
infer_results.append((int(sample_cout), float(metric)))
if sum([x[0] for x in infer_results]) == 0:
logging.info("infer results: 0.0")
else:
count = sum([x[0] for x in infer_results])
metric = sum([x[0] * x[1] for x in infer_results]) / count
logging.info("infer results: %f" % metric)
def _save_and_upload(self, date, fs_upload_path):
if self.trainer.save_and_upload_func is None:
return
if fs_upload_path is None:
return
dfs_upload_path = fs_upload_path + date + "_" + str(
self.role_maker.simulator_idx())
global_param_dict = self.scheduler_client.get_global_params()
arg_dict = {
"date": date,
"global_params": global_param_dict,
"user_param_names": self.trainer.get_user_param_names(),
"global_param_names": self.trainer.get_global_param_names(),
}
self.trainer.save_and_upload_func(
arg_dict, self.trainer.trainer_config, dfs_upload_path)
def run_simulation(self,
base_path,
dates,
fs_upload_path=None,
sim_num_everyday=1,
do_test=False,
test_skip_day=6):
if not self.role_maker.is_simulator():
pass
data_services = self._get_data_services()
for service in data_services:
service.start()
self._barrier_simulators()
self.data_client = DataClient()
self.data_client.set_load_data_into_patch_func(
self.trainer.get_load_data_into_patch_func())
self.data_client.set_data_server_endpoints(
self.role_maker.get_data_server_endpoints())
self.scheduler_client = SchedulerClient()
self.scheduler_client.set_data_server_endpoints(
self.role_maker.get_data_server_endpoints())
self.scheduler_client.set_scheduler_server_endpoints(
[self.role_maker.get_global_scheduler_endpoint()])
logging.info("trainer config: ", self.trainer.trainer_config)
self.trainer.prepare(do_test=do_test)
if self.role_maker.simulator_idx() == 0:
self.trainer.init_global_model(self.scheduler_client)
self._barrier_simulators()
for date_idx, date in enumerate(dates):
if date_idx > 0:
self.do_profile = False
self.profile_file.close()
logging.info("reading data for date: %s" % date)
local_files = self._profile(
self.data_client.get_local_files,
base_path,
date,
self.role_maker.simulator_idx(),
self.role_maker.simulator_num(),
hdfs_configs=self.hdfs_configs)
logging.info("loading data into patch for date: %s" % date)
data_patch, local_user_dict = self._profile(
self.data_client.load_data_into_patch, local_files, 10000)
logging.info("shuffling data for date: %s" % date)
self._profile(self.data_client.global_shuffle_by_patch, data_patch,
date, 30)
logging.info("updating user inst num for date: %s" % date)
self._profile(self.scheduler_client.update_user_inst_num, date,
local_user_dict)
self.role_maker.barrier_simulator()
if do_test and date_idx != 0 and date_idx % test_skip_day == 0:
self._barrier_simulators()
self._profile(self._test, date)
self._barrier_simulators()
self._profile(self._save_and_upload, date, fs_upload_path)
self._run_sim(date, sim_num_everyday=sim_num_everyday)
self.role_maker.barrier_simulator()
logging.info("clear user data for date: %s" % date)
self.data_client.clear_user_data(date)
self._barrier_simulators()
logging.info("training done all date.")
logging.info("stoping scheduler")
self.scheduler_client.stop_scheduler_server()
for pro in data_services:
pro.terminate()
logging.info("after terminate for all server.")