# Copyright 2021 Huawei Technologies Co., Ltd # # 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. # ============================================================================ import argparse import ast import numpy as np import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.nn import TrainOneStepCell, WithLossCell from src.model import LeNet5 from src.adam import AdamWeightDecayOp parser = argparse.ArgumentParser(description="test_fl_lenet") parser.add_argument("--device_target", type=str, default="CPU") parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING") parser.add_argument("--ms_role", type=str, default="MS_WORKER") parser.add_argument("--worker_num", type=int, default=0) parser.add_argument("--server_num", type=int, default=1) parser.add_argument("--scheduler_ip", type=str, default="127.0.0.1") parser.add_argument("--scheduler_port", type=int, default=8113) parser.add_argument("--fl_server_port", type=int, default=6666) parser.add_argument("--start_fl_job_threshold", type=int, default=1) parser.add_argument("--start_fl_job_time_window", type=int, default=3000) parser.add_argument("--update_model_ratio", type=float, default=1.0) parser.add_argument("--update_model_time_window", type=int, default=3000) parser.add_argument("--fl_name", type=str, default="Lenet") parser.add_argument("--fl_iteration_num", type=int, default=25) parser.add_argument("--client_epoch_num", type=int, default=20) parser.add_argument("--client_batch_size", type=int, default=32) parser.add_argument("--client_learning_rate", type=float, default=0.1) parser.add_argument("--scheduler_manage_port", type=int, default=11202) parser.add_argument("--config_file_path", type=str, default="") parser.add_argument("--encrypt_type", type=str, default="NOT_ENCRYPT") # parameters for encrypt_type='DP_ENCRYPT' parser.add_argument("--dp_eps", type=float, default=50.0) parser.add_argument("--dp_delta", type=float, default=0.01) # 1/worker_num parser.add_argument("--dp_norm_clip", type=float, default=1.0) # parameters for encrypt_type='PW_ENCRYPT' parser.add_argument("--share_secrets_ratio", type=float, default=1.0) parser.add_argument("--cipher_time_window", type=int, default=300000) parser.add_argument("--reconstruct_secrets_threshold", type=int, default=3) parser.add_argument("--client_password", type=str, default="") parser.add_argument("--server_password", type=str, default="") parser.add_argument("--enable_ssl", type=ast.literal_eval, default=False) args, _ = parser.parse_known_args() device_target = args.device_target server_mode = args.server_mode ms_role = args.ms_role worker_num = args.worker_num server_num = args.server_num scheduler_ip = args.scheduler_ip scheduler_port = args.scheduler_port fl_server_port = args.fl_server_port start_fl_job_threshold = args.start_fl_job_threshold start_fl_job_time_window = args.start_fl_job_time_window update_model_ratio = args.update_model_ratio update_model_time_window = args.update_model_time_window share_secrets_ratio = args.share_secrets_ratio cipher_time_window = args.cipher_time_window reconstruct_secrets_threshold = args.reconstruct_secrets_threshold fl_name = args.fl_name fl_iteration_num = args.fl_iteration_num client_epoch_num = args.client_epoch_num client_batch_size = args.client_batch_size client_learning_rate = args.client_learning_rate scheduler_manage_port = args.scheduler_manage_port config_file_path = args.config_file_path dp_eps = args.dp_eps dp_delta = args.dp_delta dp_norm_clip = args.dp_norm_clip encrypt_type = args.encrypt_type client_password = args.client_password server_password = args.server_password enable_ssl = args.enable_ssl ctx = { "enable_fl": True, "server_mode": server_mode, "ms_role": ms_role, "worker_num": worker_num, "server_num": server_num, "scheduler_ip": scheduler_ip, "scheduler_port": scheduler_port, "fl_server_port": fl_server_port, "start_fl_job_threshold": start_fl_job_threshold, "start_fl_job_time_window": start_fl_job_time_window, "update_model_ratio": update_model_ratio, "update_model_time_window": update_model_time_window, "share_secrets_ratio": share_secrets_ratio, "cipher_time_window": cipher_time_window, "reconstruct_secrets_threshold": reconstruct_secrets_threshold, "fl_name": fl_name, "fl_iteration_num": fl_iteration_num, "client_epoch_num": client_epoch_num, "client_batch_size": client_batch_size, "client_learning_rate": client_learning_rate, "scheduler_manage_port": scheduler_manage_port, "config_file_path": config_file_path, "dp_eps": dp_eps, "dp_delta": dp_delta, "dp_norm_clip": dp_norm_clip, "encrypt_type": encrypt_type, "client_password": client_password, "server_password": server_password, "enable_ssl": enable_ssl } context.set_context(mode=context.GRAPH_MODE, device_target=device_target) context.set_fl_context(**ctx) if __name__ == "__main__": epoch = 5 np.random.seed(0) network = LeNet5(62) criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9) net_adam_opt = AdamWeightDecayOp(network.trainable_params(), weight_decay=0.1) net_with_criterion = WithLossCell(network, criterion) train_network = TrainOneStepCell(net_with_criterion, net_opt) train_network.set_train() losses = [] for _ in range(epoch): data = Tensor(np.random.rand(32, 3, 32, 32).astype(np.float32)) label = Tensor(np.random.randint(0, 61, (32)).astype(np.int32)) loss = train_network(data, label).asnumpy() losses.append(loss) print(losses)