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Add test case for cross-silo lenet

This commit is contained in:
ZPaC 2021-07-23 16:30:47 +08:00
parent 349d3f85cb
commit fc8a16a149
8 changed files with 527 additions and 0 deletions
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6
tests/st/fl/cross_silo_lenet/config.json Normal file
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{
"recovery": {
"storge_type": 1,
"storage_file_path": "recovery.json"
}
}

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tests/st/fl/cross_silo_lenet/finish_cross_silo_lenet.py Normal file
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# 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 subprocess
parser = argparse.ArgumentParser(description="Finish test_cross_silo_lenet.py case")
parser.add_argument("--scheduler_port", type=int, default=8113)
args, _ = parser.parse_known_args()
scheduler_port = args.scheduler_port
cmd = "pid=`ps -ef|grep \"scheduler_port=" + str(scheduler_port) + "\" "
cmd += " | grep -v \"grep\" | grep -v \"finish\" |awk '{print $2}'` && "
cmd += "for id in $pid; do kill -9 $id && echo \"killed $id\"; done"
subprocess.call(['bash', '-c', cmd])

54
tests/st/fl/cross_silo_lenet/run_cross_silo_lenet_sched.py Normal file
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# 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 subprocess
parser = argparse.ArgumentParser(description="Run test_cross_silo_lenet.py case")
parser.add_argument("--device_target", type=str, default="CPU")
parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING")
parser.add_argument("--worker_num", type=int, default=1)
parser.add_argument("--server_num", type=int, default=2)
parser.add_argument("--scheduler_ip", type=str, default="127.0.0.1")
parser.add_argument("--scheduler_port", type=int, default=8113)
parser.add_argument("--scheduler_manage_port", type=int, default=11202)
parser.add_argument("--config_file_path", type=str, default="")
args, _ = parser.parse_known_args()
device_target = args.device_target
server_mode = args.server_mode
worker_num = args.worker_num
server_num = args.server_num
scheduler_ip = args.scheduler_ip
scheduler_port = args.scheduler_port
scheduler_manage_port = args.scheduler_manage_port
config_file_path = args.config_file_path
cmd_sched = "execute_path=$(pwd) && self_path=$(dirname \"${script_self}\") && rm -rf ${execute_path}/scheduler/ &&"
cmd_sched += "mkdir ${execute_path}/scheduler/ &&"
cmd_sched += "cd ${execute_path}/scheduler/ || exit && export GLOG_v=1 &&"
cmd_sched += "python ${self_path}/../test_cross_silo_lenet.py"
cmd_sched += " --device_target=" + device_target
cmd_sched += " --server_mode=" + server_mode
cmd_sched += " --ms_role=MS_SCHED"
cmd_sched += " --worker_num=" + str(worker_num)
cmd_sched += " --server_num=" + str(server_num)
cmd_sched += " --config_file_path=" + str(config_file_path)
cmd_sched += " --scheduler_ip=" + scheduler_ip
cmd_sched += " --scheduler_port=" + str(scheduler_port)
cmd_sched += " --scheduler_manage_port=" + str(scheduler_manage_port)
cmd_sched += " > scheduler.log 2>&1 &"
subprocess.call(['bash', '-c', cmd_sched])

115
tests/st/fl/cross_silo_lenet/run_cross_silo_lenet_server.py Normal file
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# 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 subprocess
parser = argparse.ArgumentParser(description="Run test_cross_silo_lenet.py case")
parser.add_argument("--device_target", type=str, default="CPU")
parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING")
parser.add_argument("--worker_num", type=int, default=1)
parser.add_argument("--server_num", type=int, default=2)
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("--local_server_num", type=int, default=-1)
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)
args, _ = parser.parse_known_args()
device_target = args.device_target
server_mode = args.server_mode
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
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
local_server_num = args.local_server_num
config_file_path = args.config_file_path
encrypt_type = args.encrypt_type
share_secrets_ratio = args.share_secrets_ratio
cipher_time_window = args.cipher_time_window
reconstruct_secrets_threshold = args.reconstruct_secrets_threshold
dp_eps = args.dp_eps
dp_delta = args.dp_delta
dp_norm_clip = args.dp_norm_clip
if local_server_num == -1:
local_server_num = server_num
assert local_server_num <= server_num, "The local server number should not be bigger than total server number."
for i in range(local_server_num):
cmd_server = "execute_path=$(pwd) && self_path=$(dirname \"${script_self}\") && "
cmd_server += "rm -rf ${execute_path}/server_" + str(i) + "/ &&"
cmd_server += "mkdir ${execute_path}/server_" + str(i) + "/ &&"
cmd_server += "cd ${execute_path}/server_" + str(i) + "/ || exit && export GLOG_v=1 &&"
cmd_server += "python ${self_path}/../test_cross_silo_lenet.py"
cmd_server += " --device_target=" + device_target
cmd_server += " --server_mode=" + server_mode
cmd_server += " --ms_role=MS_SERVER"
cmd_server += " --worker_num=" + str(worker_num)
cmd_server += " --server_num=" + str(server_num)
cmd_server += " --scheduler_ip=" + scheduler_ip
cmd_server += " --scheduler_port=" + str(scheduler_port)
cmd_server += " --fl_server_port=" + str(fl_server_port + i)
cmd_server += " --start_fl_job_threshold=" + str(start_fl_job_threshold)
cmd_server += " --start_fl_job_time_window=" + str(start_fl_job_time_window)
cmd_server += " --update_model_ratio=" + str(update_model_ratio)
cmd_server += " --update_model_time_window=" + str(update_model_time_window)
cmd_server += " --fl_name=" + fl_name
cmd_server += " --fl_iteration_num=" + str(fl_iteration_num)
cmd_server += " --config_file_path=" + str(config_file_path)
cmd_server += " --client_epoch_num=" + str(client_epoch_num)
cmd_server += " --client_batch_size=" + str(client_batch_size)
cmd_server += " --client_learning_rate=" + str(client_learning_rate)
cmd_server += " --encrypt_type=" + str(encrypt_type)
cmd_server += " --share_secrets_ratio=" + str(share_secrets_ratio)
cmd_server += " --cipher_time_window=" + str(cipher_time_window)
cmd_server += " --reconstruct_secrets_threshold=" + str(reconstruct_secrets_threshold)
cmd_server += " --dp_eps=" + str(dp_eps)
cmd_server += " --dp_delta=" + str(dp_delta)
cmd_server += " --dp_norm_clip=" + str(dp_norm_clip)
cmd_server += " > server.log 2>&1 &"
import time
time.sleep(0.3)
subprocess.call(['bash', '-c', cmd_server])

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tests/st/fl/cross_silo_lenet/run_cross_silo_lenet_worker.py Normal file
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# 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 subprocess
parser = argparse.ArgumentParser(description="Run test_cross_silo_lenet.py case")
parser.add_argument("--device_target", type=str, default="GPU")
parser.add_argument("--server_mode", type=str, default="FEDERATED_LEARNING")
parser.add_argument("--worker_num", type=int, default=1)
parser.add_argument("--server_num", type=int, default=2)
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_iteration_num", type=int, default=25)
parser.add_argument("--client_epoch_num", type=int, default=20)
parser.add_argument("--worker_step_num_per_iteration", type=int, default=65)
parser.add_argument("--local_worker_num", type=int, default=-1)
parser.add_argument("--config_file_path", type=str, default="")
args, _ = parser.parse_known_args()
device_target = args.device_target
server_mode = args.server_mode
worker_num = args.worker_num
server_num = args.server_num
scheduler_ip = args.scheduler_ip
scheduler_port = args.scheduler_port
fl_iteration_num = args.fl_iteration_num
client_epoch_num = args.client_epoch_num
worker_step_num_per_iteration = args.worker_step_num_per_iteration
local_worker_num = args.local_worker_num
config_file_path = args.config_file_path
if local_worker_num == -1:
local_worker_num = worker_num
assert local_worker_num <= worker_num, "The local worker number should not be bigger than total worker number."
for i in range(local_worker_num):
cmd_worker = "execute_path=$(pwd) && self_path=$(dirname \"${script_self}\") && "
cmd_worker += "rm -rf ${execute_path}/worker_" + str(i) + "/ &&"
cmd_worker += "mkdir ${execute_path}/worker_" + str(i) + "/ &&"
cmd_worker += "cd ${execute_path}/worker_" + str(i) + "/ || exit && export GLOG_v=1 && "
cmd_worker += "export CUDA_VISIBLE_DEVICES=" + str(i) +" && "
cmd_worker += "python ${self_path}/../test_cross_silo_lenet.py"
cmd_worker += " --device_target=" + device_target
cmd_worker += " --server_mode=" + server_mode
cmd_worker += " --ms_role=MS_WORKER"
cmd_worker += " --worker_num=" + str(worker_num)
cmd_worker += " --server_num=" + str(server_num)
cmd_worker += " --scheduler_ip=" + scheduler_ip
cmd_worker += " --scheduler_port=" + str(scheduler_port)
cmd_worker += " --config_file_path=" + str(config_file_path)
cmd_worker += " --fl_iteration_num=" + str(fl_iteration_num)
cmd_worker += " --client_epoch_num=" + str(client_epoch_num)
cmd_worker += " --worker_step_num_per_iteration=" + str(worker_step_num_per_iteration)
cmd_worker += " > worker.log 2>&1 &"
subprocess.call(['bash', '-c', cmd_worker])

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tests/st/fl/cross_silo_lenet/src/cell_wrapper.py Normal file
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# 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.
# ============================================================================
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.nn import TrainOneStepCell
class TrainOneStepCellForFLWorker(TrainOneStepCell):
"""
Wraps the network with federated learning operators in worker.
"""
def __init__(self, network, optimizer, sens=1.0, batch_size=32):
super(TrainOneStepCellForFLWorker, self).__init__(network, optimizer, sens)
self.batch_size = batch_size
self.start_fl_job = P.StartFLJob(batch_size)
self.update_model = P.UpdateModel()
self.get_model = P.GetModel()
self.depend = P.Depend()
def construct(self, *inputs):
start_fl_job = self.start_fl_job()
inputs = self.depend(inputs, start_fl_job)
loss = self.network(*inputs)
sens = F.fill(loss.dtype, loss.shape, self.sens)
grads = self.grad(self.network, self.weights)(*inputs, sens)
grads = self.grad_reducer(grads)
loss = self.depend(loss, self.optimizer(grads))
self.update_model(self.weights)
get_model = self.get_model(self.weights)
loss = self.depend(loss, get_model)
return loss

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tests/st/fl/cross_silo_lenet/src/model.py Normal file
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# 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 mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
"""weight initial for conv layer"""
weight = weight_variable()
return nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
weight_init=weight,
has_bias=False,
pad_mode="valid",
)
def fc_with_initialize(input_channels, out_channels):
"""weight initial for fc layer"""
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
"""weight initial"""
return TruncatedNormal(0.02)
class LeNet5(nn.Cell):
def __init__(self, num_class=10, channel=3):
super(LeNet5, self).__init__()
self.num_class = num_class
self.conv1 = conv(channel, 6, 5)
self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, self.num_class)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x

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tests/st/fl/cross_silo_lenet/test_cross_silo_lenet.py Normal file
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# 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 numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.nn import WithLossCell
from src.cell_wrapper import TrainOneStepCellForFLWorker
from src.model import LeNet5
parser = argparse.ArgumentParser(description="test_cross_silo_lenet")
parser.add_argument("--device_target", type=str, default="GPU")
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=1)
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("--worker_step_num_per_iteration", type=int, default=65)
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)
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
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
worker_step_num_per_iteration = args.worker_step_num_per_iteration
scheduler_manage_port = args.scheduler_manage_port
config_file_path = args.config_file_path
encrypt_type = args.encrypt_type
share_secrets_ratio = args.share_secrets_ratio
cipher_time_window = args.cipher_time_window
reconstruct_secrets_threshold = args.reconstruct_secrets_threshold
dp_eps = args.dp_eps
dp_delta = args.dp_delta
dp_norm_clip = args.dp_norm_clip
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,
"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,
"worker_step_num_per_iteration": worker_step_num_per_iteration,
"scheduler_manage_port": scheduler_manage_port,
"config_file_path": config_file_path,
"share_secrets_ratio": share_secrets_ratio,
"cipher_time_window": cipher_time_window,
"reconstruct_secrets_threshold": reconstruct_secrets_threshold,
"dp_eps": dp_eps,
"dp_delta": dp_delta,
"dp_norm_clip": dp_norm_clip,
"encrypt_type": encrypt_type
}
context.set_context(mode=context.GRAPH_MODE, device_target=device_target, save_graphs=False)
context.set_fl_context(**ctx)
if __name__ == "__main__":
epoch = 50000
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_with_criterion = WithLossCell(network, criterion)
train_network = TrainOneStepCellForFLWorker(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)