forked from mindspore-Ecosystem/mindspore
247 lines
11 KiB
Python
247 lines
11 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import argparse
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import os
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import sys
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import ast
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from time import time
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from src.config import train_cfg, server_net_cfg
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from src.model import AlbertModelCLS
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from src.cell_wrapper import NetworkWithCLSLoss, NetworkTrainCell
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def parse_args():
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"""
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parse args
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"""
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parser = argparse.ArgumentParser(description='server task')
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parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU', 'CPU'])
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parser.add_argument('--device_id', type=str, default='0')
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parser.add_argument('--tokenizer_dir', type=str, default='../model_save/init/')
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parser.add_argument('--server_data_path', type=str, default='../datasets/semi_supervise/server/train.txt')
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parser.add_argument('--model_path', type=str, default='../model_save/init/albert_init.ckpt')
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parser.add_argument('--output_dir', type=str, default='../model_save/train_server/')
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parser.add_argument('--vocab_map_ids_path', type=str, default='../model_save/init/vocab_map_ids.txt')
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parser.add_argument('--logging_step', type=int, default=1)
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parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING")
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parser.add_argument("--ms_role", type=str, default="MS_WORKER")
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parser.add_argument("--worker_num", type=int, default=0)
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parser.add_argument("--server_num", type=int, default=1)
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parser.add_argument("--scheduler_ip", type=str, default="127.0.0.1")
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parser.add_argument("--scheduler_port", type=int, default=8113)
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parser.add_argument("--fl_server_port", type=int, default=6666)
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parser.add_argument("--start_fl_job_threshold", type=int, default=1)
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parser.add_argument("--start_fl_job_time_window", type=int, default=3000)
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parser.add_argument("--update_model_ratio", type=float, default=1.0)
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parser.add_argument("--update_model_time_window", type=int, default=3000)
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parser.add_argument("--fl_name", type=str, default="Lenet")
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parser.add_argument("--fl_iteration_num", type=int, default=25)
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parser.add_argument("--client_epoch_num", type=int, default=20)
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parser.add_argument("--client_batch_size", type=int, default=32)
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parser.add_argument("--client_learning_rate", type=float, default=0.1)
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parser.add_argument("--worker_step_num_per_iteration", type=int, default=65)
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parser.add_argument("--scheduler_manage_port", type=int, default=11202)
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parser.add_argument("--dp_eps", type=float, default=50.0)
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parser.add_argument("--dp_delta", type=float, default=0.01) # usually equals 1/start_fl_job_threshold
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parser.add_argument("--dp_norm_clip", type=float, default=1.0)
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parser.add_argument("--encrypt_type", type=str, default="NOT_ENCRYPT")
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parser.add_argument("--share_secrets_ratio", type=float, default=1.0)
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parser.add_argument("--cipher_time_window", type=int, default=300000)
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parser.add_argument("--reconstruct_secrets_threshold", type=int, default=3)
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parser.add_argument("--config_file_path", type=str, default="")
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parser.add_argument("--client_password", type=str, default="")
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parser.add_argument("--server_password", type=str, default="")
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parser.add_argument("--enable_ssl", type=ast.literal_eval, default=False)
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parser.add_argument("--pki_verify", type=ast.literal_eval, default=False)
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# parameters used for pki_verify=True
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parser.add_argument("--root_first_ca_path", type=str, default="")
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parser.add_argument("--root_second_ca_path", type=str, default="")
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parser.add_argument("--equip_crl_path", type=str, default="")
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parser.add_argument("--replay_attack_time_diff", type=int, default=600000)
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# parameters for 'SIGNDS'
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parser.add_argument("--sign_k", type=float, default=0.01)
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parser.add_argument("--sign_eps", type=float, default=100)
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parser.add_argument("--sign_thr_ratio", type=float, default=0.6)
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parser.add_argument("--sign_global_lr", type=float, default=0.1)
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parser.add_argument("--sign_dim_out", type=int, default=0)
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parser.add_argument("--global_iteration_time_window", type=int, default=3600000)
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# parameters for "compression"
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parser.add_argument("--upload_compress_type", type=str, default="NO_COMPRESS",
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choices=["NO_COMPRESS", "DIFF_SPARSE_QUANT"])
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parser.add_argument("--upload_sparse_rate", type=float, default=0.5)
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parser.add_argument("--download_compress_type", type=str, default="NO_COMPRESS",
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choices=["NO_COMPRESS", "QUANT"])
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return parser.parse_args()
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def server_train(args):
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start = time()
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os.environ['CUDA_VISIBLE_DEVICES'] = args.device_id
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device_target = args.device_target
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server_mode = args.server_mode
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ms_role = args.ms_role
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worker_num = args.worker_num
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server_num = args.server_num
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scheduler_ip = args.scheduler_ip
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scheduler_port = args.scheduler_port
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fl_server_port = args.fl_server_port
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start_fl_job_threshold = args.start_fl_job_threshold
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start_fl_job_time_window = args.start_fl_job_time_window
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update_model_ratio = args.update_model_ratio
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update_model_time_window = args.update_model_time_window
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fl_name = args.fl_name
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fl_iteration_num = args.fl_iteration_num
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client_epoch_num = args.client_epoch_num
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client_batch_size = args.client_batch_size
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client_learning_rate = args.client_learning_rate
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scheduler_manage_port = args.scheduler_manage_port
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dp_delta = args.dp_delta
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dp_norm_clip = args.dp_norm_clip
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encrypt_type = args.encrypt_type
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share_secrets_ratio = args.share_secrets_ratio
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cipher_time_window = args.cipher_time_window
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reconstruct_secrets_threshold = args.reconstruct_secrets_threshold
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config_file_path = args.config_file_path
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client_password = args.client_password
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server_password = args.server_password
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enable_ssl = args.enable_ssl
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pki_verify = args.pki_verify
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root_first_ca_path = args.root_first_ca_path
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root_second_ca_path = args.root_second_ca_path
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equip_crl_path = args.equip_crl_path
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replay_attack_time_diff = args.replay_attack_time_diff
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sign_k = args.sign_k
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sign_eps = args.sign_eps
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sign_thr_ratio = args.sign_thr_ratio
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sign_global_lr = args.sign_global_lr
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sign_dim_out = args.sign_dim_out
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global_iteration_time_window = args.global_iteration_time_window
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upload_compress_type = args.upload_compress_type
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upload_sparse_rate = args.upload_sparse_rate
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download_compress_type = args.download_compress_type
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# Replace some parameters with federated learning parameters.
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train_cfg.max_global_epoch = fl_iteration_num
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fl_ctx = {
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"enable_fl": True,
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"server_mode": server_mode,
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"ms_role": ms_role,
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"worker_num": worker_num,
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"server_num": server_num,
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"scheduler_ip": scheduler_ip,
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"scheduler_port": scheduler_port,
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"fl_server_port": fl_server_port,
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"start_fl_job_threshold": start_fl_job_threshold,
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"start_fl_job_time_window": start_fl_job_time_window,
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"update_model_ratio": update_model_ratio,
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"update_model_time_window": update_model_time_window,
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"fl_name": fl_name,
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"fl_iteration_num": fl_iteration_num,
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"client_epoch_num": client_epoch_num,
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"client_batch_size": client_batch_size,
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"client_learning_rate": client_learning_rate,
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"scheduler_manage_port": scheduler_manage_port,
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"dp_delta": dp_delta,
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"dp_norm_clip": dp_norm_clip,
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"encrypt_type": encrypt_type,
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"share_secrets_ratio": share_secrets_ratio,
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"cipher_time_window": cipher_time_window,
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"reconstruct_secrets_threshold": reconstruct_secrets_threshold,
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"config_file_path": config_file_path,
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"client_password": client_password,
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"server_password": server_password,
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"enable_ssl": enable_ssl,
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"pki_verify": pki_verify,
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"root_first_ca_path": root_first_ca_path,
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"root_second_ca_path": root_second_ca_path,
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"equip_crl_path": equip_crl_path,
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"replay_attack_time_diff": replay_attack_time_diff,
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"sign_k": sign_k,
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"sign_eps": sign_eps,
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"sign_thr_ratio": sign_thr_ratio,
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"sign_global_lr": sign_global_lr,
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"sign_dim_out": sign_dim_out,
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"global_iteration_time_window": global_iteration_time_window,
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"upload_compress_type": upload_compress_type,
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"upload_sparse_rate": upload_sparse_rate,
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"download_compress_type": download_compress_type,
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}
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# mindspore context
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ms.set_context(mode=ms.GRAPH_MODE, device_target=device_target)
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ms.set_fl_context(**fl_ctx)
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print('Context setting is done! Time cost: {}'.format(time() - start))
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sys.stdout.flush()
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start = time()
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# construct model
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albert_model_cls = AlbertModelCLS(server_net_cfg)
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network_with_cls_loss = NetworkWithCLSLoss(albert_model_cls)
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network_with_cls_loss.set_train(True)
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print('Model construction is done! Time cost: {}'.format(time() - start))
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sys.stdout.flush()
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start = time()
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# server optimizer
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server_params = [_ for _ in network_with_cls_loss.trainable_params()]
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server_decay_params = list(
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filter(train_cfg.optimizer_cfg.AdamWeightDecay.decay_filter, server_params)
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)
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server_other_params = list(
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filter(lambda x: not train_cfg.optimizer_cfg.AdamWeightDecay.decay_filter(x), server_params)
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)
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server_group_params = [
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{'params': server_decay_params, 'weight_decay': train_cfg.optimizer_cfg.AdamWeightDecay.weight_decay},
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{'params': server_other_params, 'weight_decay': 0.0},
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{'order_params': server_params}
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]
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server_optimizer = nn.Adam(server_group_params,
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learning_rate=train_cfg.server_cfg.learning_rate,
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eps=train_cfg.optimizer_cfg.AdamWeightDecay.eps)
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server_network_train_cell = NetworkTrainCell(network_with_cls_loss, optimizer=server_optimizer)
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print('Optimizer construction is done! Time cost: {}'.format(time() - start))
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sys.stdout.flush()
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start = time()
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# train process
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for _ in range(1):
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input_ids = ms.Tensor(np.zeros((train_cfg.batch_size, server_net_cfg.seq_length), np.int32))
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attention_mask = ms.Tensor(np.zeros((train_cfg.batch_size, server_net_cfg.seq_length), np.int32))
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token_type_ids = ms.Tensor(np.zeros((train_cfg.batch_size, server_net_cfg.seq_length), np.int32))
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label_ids = ms.Tensor(np.zeros((train_cfg.batch_size,), np.int32))
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model_start_time = time()
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cls_loss = server_network_train_cell(input_ids, attention_mask, token_type_ids, label_ids)
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time_cost = time() - model_start_time
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print('server: cls_loss {} time_cost {}'.format(cls_loss, time_cost))
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sys.stdout.flush()
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del input_ids, attention_mask, token_type_ids, label_ids, cls_loss
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print('Training process is done! Time cost: {}'.format(time() - start))
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if __name__ == '__main__':
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args_opt = parse_args()
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server_train(args_opt)
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