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main.py
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main.py
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import argparse
import os
import signal
import subprocess
import time
import torch
import torch.optim as optim
torch.set_num_threads(1)
import syft as sy
from syft.serde.compression import NO_COMPRESSION
from syft.grid.clients.data_centric_fl_client import DataCentricFLClient
sy.serde.compression.default_compress_scheme = NO_COMPRESSION
from procedure import train, test
from data import get_data_loaders, get_number_classes
from models import get_model, load_state_dict
from preprocess import build_prepocessing
def run(args):
if args.train:
print(f"Training over {args.epochs} epochs")
elif args.test:
print("Running a full evaluation")
else:
print("Running inference speed test")
print("model:\t\t", args.model)
print("dataset:\t", args.dataset)
print("batch_size:\t", args.batch_size)
hook = sy.TorchHook(torch)
if args.websockets:
alice = DataCentricFLClient(hook, "ws://localhost:7600")
bob = DataCentricFLClient(hook, "ws://localhost:7601")
crypto_provider = DataCentricFLClient(hook, "ws://localhost:7602")
my_grid = sy.PrivateGridNetwork(alice, bob, crypto_provider)
sy.local_worker.object_store.garbage_delay = 1
else:
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")
crypto_provider = sy.VirtualWorker(hook, id="crypto_provider")
workers = [alice, bob]
sy.local_worker.clients = workers
encryption_kwargs = dict(
workers=workers, crypto_provider=crypto_provider, protocol=args.protocol
)
kwargs = dict(
requires_grad=args.requires_grad,
precision_fractional=args.precision_fractional,
dtype=args.dtype,
**encryption_kwargs,
)
if args.preprocess:
build_prepocessing(args.model, args.dataset, args.batch_size, workers, args)
private_train_loader, private_test_loader = get_data_loaders(args, kwargs, private=True)
public_train_loader, public_test_loader = get_data_loaders(args, kwargs, private=False)
model = get_model(args.model, args.dataset, out_features=get_number_classes(args.dataset))
if args.test and not args.train:
load_state_dict(model, args.model, args.dataset)
model.eval()
if torch.cuda.is_available():
sy.cuda_force = True
if not args.public:
model.encrypt(**kwargs)
if args.fp_only: # Just keep the (Autograd+) Fixed Precision feature
model.get()
if args.train:
for epoch in range(args.epochs):
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
if not args.public:
optimizer = optimizer.fix_precision(
precision_fractional=args.precision_fractional, dtype=args.dtype
)
train_time = train(args, model, private_train_loader, optimizer, epoch)
test_time, accuracy = test(args, model, private_test_loader)
else:
test_time, accuracy = test(args, model, private_test_loader)
if not args.test:
print(
f"{ 'Online' if args.preprocess else 'Total' } time (s):\t",
round(test_time / args.batch_size, 4),
)
else:
# Compare with clear text accuracy
print("Clear text accuracy is:")
model = get_model(
args.model, args.dataset, out_features=get_number_classes(args.dataset)
)
load_state_dict(model, args.model, args.dataset)
test(args, model, public_test_loader)
if args.preprocess:
missing_items = [len(v) for k, v in sy.preprocessed_material.items()]
if sum(missing_items) > 0:
print("MISSING preprocessed material")
for key, value in sy.preprocessed_material.items():
print(f"'{key}':", value, ",")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
type=str,
help="model to use for inference (network1, network2, lenet, alexnet, vgg16, resnet18)",
)
parser.add_argument(
"--dataset",
type=str,
help="dataset to use (mnist, cifar10, hymenoptera, tiny-imagenet)",
)
parser.add_argument(
"--batch_size",
type=int,
help="size of the batch to use. Default 128.",
default=128,
)
parser.add_argument(
"--test_batch_size",
type=int,
help="size of the batch to use",
default=None,
)
parser.add_argument(
"--preprocess",
help="[only for speed test] preprocess data or not",
action="store_true",
)
parser.add_argument(
"--fp_only",
help="Don't secret share values, just convert them to fix precision",
action="store_true",
)
parser.add_argument(
"--public",
help="[needs --train] Train without fix precision or secret sharing",
action="store_true",
)
parser.add_argument(
"--test",
help="run testing on the complete test dataset",
action="store_true",
)
parser.add_argument(
"--train",
help="run training for n epochs",
action="store_true",
)
parser.add_argument(
"--epochs",
type=int,
help="[needs --train] number of epochs to train on. Default 15.",
default=15,
)
parser.add_argument(
"--lr",
type=float,
help="[needs --train] learning rate of the SGD. Default 0.01.",
default=0.01,
)
parser.add_argument(
"--momentum",
type=float,
help="[needs --train] momentum of the SGD. Default 0.9.",
default=0.9,
)
parser.add_argument(
"--websockets",
help="use PyGrid nodes instead of a virtual network. (nodes are launched automatically)",
action="store_true",
)
parser.add_argument(
"--verbose",
help="show extra information and metrics",
action="store_true",
)
parser.add_argument(
"--log_interval",
type=int,
help="[needs --test or --train] log intermediate metrics every n batches. Default 10.",
default=10,
)
parser.add_argument(
"--comm_info",
help="Print communication information",
action="store_true",
)
parser.add_argument(
"--pyarrow_info",
help="print information about PyArrow usage and failure",
action="store_true",
)
cmd_args = parser.parse_args()
# Sanity checks
if cmd_args.test or cmd_args.train:
assert (
not cmd_args.preprocess
), "Can't preprocess for a full epoch evaluation or training, remove --preprocess"
if cmd_args.train:
assert not cmd_args.test, "Can't set --test if you already have --train"
if cmd_args.fp_only:
assert not cmd_args.preprocess, "Can't have --preprocess in a fixed precision setting"
assert not cmd_args.public, "Can't have simultaneously --fp_only and --public"
if not cmd_args.train:
assert not cmd_args.public, "--public is used only for training"
if cmd_args.pyarrow_info:
sy.pyarrow_info = True
class Arguments:
model = cmd_args.model.lower()
dataset = cmd_args.dataset.lower()
preprocess = cmd_args.preprocess
websockets = cmd_args.websockets
verbose = cmd_args.verbose
train = cmd_args.train
n_train_items = -1 if cmd_args.train else cmd_args.batch_size
test = cmd_args.test or cmd_args.train
n_test_items = -1 if cmd_args.test or cmd_args.train else cmd_args.batch_size
batch_size = cmd_args.batch_size
# Defaults to the train batch_size
test_batch_size = cmd_args.test_batch_size or cmd_args.batch_size
log_interval = cmd_args.log_interval
comm_info = cmd_args.comm_info
epochs = cmd_args.epochs
lr = cmd_args.lr
momentum = cmd_args.momentum
public = cmd_args.public
fp_only = cmd_args.fp_only
requires_grad = cmd_args.train
dtype = "long"
protocol = "fss"
precision_fractional = 5 if cmd_args.train else 4
args = Arguments()
if args.websockets:
print("Launching the websocket workers...")
def kill_processes(worker_processes):
for worker_process in worker_processes:
pid = worker_process.pid
try:
os.killpg(os.getpgid(worker_process.pid), signal.SIGTERM)
print(f"Process {pid} killed")
except ProcessLookupError:
print(f"COULD NOT KILL PROCESS {pid}")
worker_processes = [
subprocess.Popen(
f"./scripts/launch_{worker}.sh",
stdout=subprocess.PIPE,
shell=True,
preexec_fn=os.setsid,
executable="/bin/bash",
)
for worker in ["alice", "bob", "crypto_provider"]
]
time.sleep(7)
try:
print("LAUNCHED", *[p.pid for p in worker_processes])
run(args)
kill_processes(worker_processes)
except Exception as e:
kill_processes(worker_processes)
raise e
else:
run(args)