SPU 训练神经网络#
请先阅读 Logistic Regression On SPU 。
在实验 Logistic Regression On SPU 中，我们展示了如何使用 SecretFlow/SPU 将明文 JAX 训练程序转换为安全 MPC 训练程序。
在这个实验室中，这个想法非常相似，但这次我们将使用神经网络模型。
我们将使用相同的数据集和所有设置。
首先，让我们制定明文模型。
以下代码仅作为示例，请勿在生产环境直接使用。
本教程需要比 8c16g 更多的资源，这是 SecretFlow 的最低要求。
使用 JAX/FLAX 训练模型#
加载数据集#
以下内容是复制于实验 Logistic Regression On SPU 。 我不会再次在这里解释。
[ ]:
import sys
!{sys.executable} -m pip install flax==0.6.0
[1]:
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Normalizer
def breast_cancer(party_id=None, train: bool = True) -> (np.ndarray, np.ndarray):
scaler = Normalizer(norm='max')
x, y = load_breast_cancer(return_X_y=True)
x = scaler.fit_transform(x)
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=42
)
if train:
if party_id:
if party_id == 1:
return x_train[:, 15:], _
else:
return x_train[:, :15], y_train
else:
return x_train, y_train
else:
return x_test, y_test
定义模型#
[2]:
from typing import Sequence
import flax.linen as nn
FEATURES = [30, 15, 8, 1]
class MLP(nn.Module):
features: Sequence[int]
@nn.compact
def __call__(self, x):
for feat in self.features[:-1]:
x = nn.relu(nn.Dense(feat)(x))
x = nn.Dense(self.features[-1])(x)
return x
然后我们在这里定义训练方法。
[3]:
import jax.numpy as jnp
def predict(params, x):
# TODO(junfeng): investigate why need to have a duplicated definition in notebook,
# which is not the case in a normal python program.
from typing import Sequence
import flax.linen as nn
FEATURES = [30, 15, 8, 1]
class MLP(nn.Module):
features: Sequence[int]
@nn.compact
def __call__(self, x):
for feat in self.features[:-1]:
x = nn.relu(nn.Dense(feat)(x))
x = nn.Dense(self.features[-1])(x)
return x
return MLP(FEATURES).apply(params, x)
def loss_func(params, x, y):
pred = predict(params, x)
def mse(y, pred):
def squared_error(y, y_pred):
return jnp.multiply(y - y_pred, y - y_pred) / 2.0
return jnp.mean(squared_error(y, pred))
return mse(y, pred)
def train_auto_grad(x1, x2, y, params, n_batch=10, n_epochs=10, step_size=0.01):
x = jnp.concatenate((x1, x2), axis=1)
xs = jnp.array_split(x, len(x) / n_batch, axis=0)
ys = jnp.array_split(y, len(y) / n_batch, axis=0)
def body_fun(_, loop_carry):
params = loop_carry
for (x, y) in zip(xs, ys):
_, grads = jax.value_and_grad(loss_func)(params, x, y)
params = jax.tree_util.tree_map(
lambda p, g: p - step_size * g, params, grads
)
return params
params = jax.lax.fori_loop(0, n_epochs, body_fun, params)
return params
def model_init(n_batch=10):
model = MLP(FEATURES)
return model.init(jax.random.PRNGKey(1), jnp.ones((n_batch, FEATURES[0])))
验证模型#
我们使用 AUC 作为验证指标。
[4]:
from sklearn.metrics import roc_auc_score
def validate_model(params, X_test, y_test):
y_pred = predict(params, X_test)
return roc_auc_score(y_test, y_pred)
放在一起#
让我们把所有不住放在一起，训练一个明文 NN 模型！
[5]:
import jax
# Load the data
x1, _ = breast_cancer(party_id=1, train=True)
x2, y = breast_cancer(party_id=2, train=True)
# Hyperparameter
n_batch = 10
n_epochs = 10
step_size = 0.01
# Train the model
init_params = model_init(n_batch)
params = train_auto_grad(x1, x2, y, init_params, n_batch, n_epochs, step_size)
# Test the model
X_test, y_test = breast_cancer(train=False)
auc = validate_model(params, X_test, y_test)
print(f'auc={auc}')
WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
auc=0.9921388797903701
让我们把所有东西放在一起，训练一个明文神经网络模型！
使用 SPU 训练模型#
[6]:
import secretflow as sf
# In case you have a running secretflow runtime already.
sf.shutdown()
sf.init(['alice', 'bob'], address='local')
alice, bob = sf.PYU('alice'), sf.PYU('bob')
spu = sf.SPU(sf.utils.testing.cluster_def(['alice', 'bob']))
x1, _ = alice(breast_cancer)(party_id=1, train=True)
x2, y = bob(breast_cancer)(party_id=2, train=True)
init_params = model_init(n_batch)
device = spu
x1_, x2_, y_ = x1.to(device), x2.to(device), y.to(device)
init_params_ = sf.to(alice, init_params).to(device)
params_spu = spu(train_auto_grad, static_argnames=['n_batch', 'n_epochs', 'step_size'])(
x1_, x2_, y_, init_params_, n_batch=n_batch, n_epochs=n_epochs, step_size=step_size
)
2022-08-25 10:54:11.675312: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944990) 2022-08-25 10:54:17.053663: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944994) 2022-08-25 10:54:17.053663: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944991) 2022-08-25 10:54:17.053663: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944992) 2022-08-25 10:54:17.064685: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944996) 2022-08-25 10:54:17.053663: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(pid=2944988) 2022-08-25 10:54:17.011062: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(_run pid=2944995) 2022-08-25 10:54:17.145220: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(_run pid=2944993) 2022-08-25 10:54:17.154674: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/rh/rh-ruby25/root/usr/local/lib64:/opt/rh/rh-ruby25/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib64:/opt/rh/devtoolset-11/root/usr/lib:/opt/rh/devtoolset-11/root/usr/lib64/dyninst:/opt/rh/devtoolset-11/root/usr/lib/dyninst
(SPURuntime pid=2944988) I0825 10:54:18.801868 2944988 external/com_github_brpc_brpc/src/brpc/server.cpp:1066] Server[yacl::link::internal::ReceiverServiceImpl] is serving on port=44351.
(SPURuntime pid=2944988) I0825 10:54:18.801938 2944988 external/com_github_brpc_brpc/src/brpc/server.cpp:1069] Check out http://k69b13338.eu95sqa:44351 in web browser.
(_run pid=2944995) 2022-08-25 10:54:18,906,906 WARNING [xla_bridge.py:backends:265] No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
(SPURuntime pid=2944990) I0825 10:54:18.863563 2944990 external/com_github_brpc_brpc/src/brpc/server.cpp:1066] Server[yacl::link::internal::ReceiverServiceImpl] is serving on port=22227.
(SPURuntime pid=2944990) I0825 10:54:18.863633 2944990 external/com_github_brpc_brpc/src/brpc/server.cpp:1069] Check out http://k69b13338.eu95sqa:22227 in web browser.
(SPURuntime pid=2944988) I0825 10:54:18.902659 2945551 external/com_github_brpc_brpc/src/brpc/socket.cpp:2202] Checking Socket{id=0 addr=127.0.0.1:22227} (0x55f83a4820c0)
(SPURuntime pid=2944988) I0825 10:54:18.902798 2945551 external/com_github_brpc_brpc/src/brpc/socket.cpp:2262] Revived Socket{id=0 addr=127.0.0.1:22227} (0x55f83a4820c0) (Connectable)
(_run pid=2944995) [2022-08-25 10:54:18.906] [info] [thread_pool.cc:30] Create a fixed thread pool with size 63
(_run pid=2944993) [2022-08-25 10:54:18.941] [info] [thread_pool.cc:30] Create a fixed thread pool with size 63
(_run pid=2944993) 2022-08-25 10:54:18,941,941 WARNING [xla_bridge.py:backends:265] No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
(_run pid=2944996) 2022-08-25 10:54:18,952,952 WARNING [xla_bridge.py:backends:265] No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
让我们检查 SPU 程序的参数。
[7]:
params_spu = spu(train_auto_grad)(x1_, x2_, y_, init_params)
params = sf.reveal(params_spu)
print(params)
(SPURuntime pid=2944988) [2022-08-25 10:54:29.343] [info] [thread_pool.cc:30] Create a fixed thread pool with size 63
(SPURuntime pid=2944990) [2022-08-25 10:54:29.328] [info] [thread_pool.cc:30] Create a fixed thread pool with size 63
FrozenDict({
params: {
Dense_0: {
bias: array([-5.1895231e-03, 1.8576235e-03, 6.7055225e-06, 2.3722053e-03,
3.1328186e-02, 6.7055225e-06, -1.5438795e-02, 6.7055225e-06,
6.7055225e-06, -4.1383177e-02, 6.7055225e-06, -2.2077844e-02,
6.7055225e-06, 6.7055225e-06, -4.1123331e-03, 6.7055225e-06,
3.9519504e-02, -2.0013824e-02, 6.7055225e-06, -1.7026097e-02,
-2.0100027e-03, -1.2290031e-02, 3.5510257e-02, 6.7055225e-06,
1.0400787e-02, 6.7055225e-06, 6.7055225e-06, 6.7055225e-06,
6.7055225e-06, -2.4091452e-03], dtype=float32),
kernel: array([[-1.48707509e-01, -2.35312745e-01, -1.49370432e-01,
-1.55658722e-02, -1.33344889e-01, 1.91762865e-01,
-3.67970318e-02, -3.74531150e-02, -1.41760916e-01,
3.24019790e-02, 1.26554981e-01, -4.02508259e-01,
-1.68948472e-01, 2.13856786e-01, -1.38430491e-01,
1.05858222e-01, -1.16115242e-01, 3.86231482e-01,
5.96616715e-02, 6.30981326e-02, 7.79366642e-02,
-1.31276995e-02, -2.88023233e-01, -9.60215628e-02,
1.11036777e-01, -8.54356140e-02, 7.54796267e-02,
-4.11889851e-02, -3.82702172e-01, 2.37623483e-01],
[ 1.77950412e-01, 2.29401365e-01, -2.44401962e-01,
-1.48464620e-01, 3.36820692e-01, 2.58408487e-02,
-4.21461910e-02, 4.10513222e-01, 3.24393630e-01,
-1.64218560e-01, 8.17326903e-02, 5.25916368e-02,
3.11348379e-01, 2.92945385e-01, 1.22726962e-01,
-3.87524545e-01, -3.85492414e-01, -6.53883070e-02,
-2.59158403e-01, -3.32410604e-01, -3.15876693e-01,
-2.91079849e-01, -6.01480752e-02, 2.29791373e-01,
1.01019114e-01, -1.30936205e-02, 1.78829342e-01,
-2.32158348e-01, 3.82750481e-01, 3.79820764e-02],
[ 1.42894119e-01, -2.13525295e-02, 1.68190986e-01,
8.99314433e-02, -3.89296085e-01, -4.85262126e-02,
1.38709426e-01, -5.80671132e-02, 2.84729242e-01,
-1.26564085e-01, 2.57159889e-01, 9.64836925e-02,
1.16704285e-01, -1.90519944e-01, -3.98443341e-02,
-9.57311541e-02, 7.24758208e-02, 1.46388173e-01,
9.22411978e-02, 3.83930653e-02, -2.46246234e-01,
3.76523137e-02, -1.90257579e-02, -2.52097666e-01,
1.70252651e-01, 2.49821901e-01, -2.81400979e-03,
-9.84508097e-02, 2.07754672e-01, 8.81182998e-02],
[-2.25118160e-01, -4.46529686e-03, -4.55757827e-02,
-4.27887589e-02, -1.34788275e-01, -3.42829585e-01,
-9.25171375e-03, 9.01048332e-02, -2.82488763e-01,
2.23106414e-01, 1.17174536e-01, 4.47563082e-02,
-5.02136499e-02, 2.90469885e-01, -2.35435143e-01,
5.99489361e-02, 2.55998850e-01, 1.87139437e-01,
1.46078974e-01, 2.93805301e-01, 3.95906270e-02,
1.63574621e-01, -1.12822607e-01, -6.96890652e-02,
2.61215031e-01, -3.17869127e-01, 8.15212727e-02,
2.55623460e-01, -5.98048419e-02, 1.93995312e-01],
[-2.42546692e-01, 7.96881765e-02, -6.76873624e-02,
-1.17468625e-01, 1.87561423e-01, -6.13799393e-02,
-5.36612719e-02, -6.93449825e-02, 7.92452097e-02,
2.54104435e-02, -3.18573356e-01, 2.87047565e-01,
-6.02750182e-02, -3.01489800e-01, -1.76609412e-01,
7.97384530e-02, 1.61419287e-01, 3.27948928e-01,
2.05150560e-01, 3.03487569e-01, 2.71105886e-01,
2.76556283e-01, 7.07161725e-02, 2.08005011e-01,
-7.33365566e-02, -1.03249148e-01, 1.55347139e-02,
3.17582250e-01, 3.16770792e-01, -6.80912733e-02],
[ 7.31387287e-02, 6.06941879e-02, 5.30773848e-02,
1.78480223e-01, 1.88692018e-01, 1.77043870e-01,
8.07239711e-02, 1.48239732e-02, -4.42493856e-02,
6.05108887e-02, 2.88271666e-01, 5.82368821e-02,
2.64274329e-01, 1.20873421e-01, -2.19291598e-02,
-1.51663244e-01, -4.52437997e-02, 6.89959526e-03,
2.83989072e-01, 1.00405067e-01, -3.03579658e-01,
-3.13743889e-01, -1.75221011e-01, 1.12235680e-01,
1.15922809e-01, -2.76054680e-01, -6.86793476e-02,
6.13613129e-02, 3.08204144e-01, -2.80041754e-01],
[-2.58817077e-01, -1.54795498e-02, 2.77136236e-01,
-3.83958876e-01, 4.00266647e-01, -1.29193932e-01,
-2.80725807e-02, 1.75729364e-01, 1.30319521e-01,
1.56524405e-01, -2.62551606e-02, 2.93114007e-01,
3.55902165e-02, 3.58770192e-02, -2.96767652e-01,
-1.69694602e-01, 3.19763869e-02, 1.61003023e-01,
-1.75322652e-01, -8.83801430e-02, -4.86088842e-02,
-1.05747893e-01, 8.46082866e-02, -4.35629487e-02,
-2.53890634e-01, -9.85630006e-02, -4.16661799e-02,
-4.67738807e-02, -3.35340977e-01, -1.18303642e-01],
[ 2.78338730e-01, -6.96775764e-02, -2.48151869e-02,
4.41243351e-02, -8.29881430e-02, -1.39038265e-03,
8.48067403e-02, -2.48347864e-01, 1.53563991e-01,
-1.36309460e-01, -4.34331596e-03, -6.34510815e-03,
-7.79297352e-02, -1.02945447e-01, 2.40225092e-01,
-2.56992996e-01, 2.98645079e-01, -1.97924539e-01,
-2.75554240e-01, 1.29482746e-02, 1.26732409e-01,
1.27245054e-01, 1.59333318e-01, 1.43134385e-01,
6.40332401e-02, -1.11376449e-01, 5.90692759e-02,
-1.16492316e-01, 1.58721358e-02, -2.03465536e-01],
[ 6.27327412e-02, 8.74824524e-02, -9.10452157e-02,
1.81233525e-01, 1.06525749e-01, -1.38279110e-01,
3.20228636e-01, -1.48054436e-01, -3.18492800e-02,
3.50982606e-01, -1.33533657e-01, 1.76203504e-01,
5.12142032e-02, -4.60760295e-02, -1.33599430e-01,
-2.22507954e-01, 1.66286990e-01, 3.71264696e-01,
-1.16020367e-01, -1.61022544e-02, -8.04677159e-02,
1.58347130e-01, 3.06297302e-01, 6.74358159e-02,
8.46540183e-02, -9.24098492e-03, -3.53501737e-03,
-3.68553460e-01, -1.23027235e-01, 2.18146190e-01],
[ 2.83379406e-02, -1.24499649e-01, 1.79811046e-01,
1.53642461e-01, 5.48425168e-02, 1.91716999e-01,
-9.49321389e-02, 6.86786622e-02, -7.67818838e-02,
-1.93986297e-02, 5.70140183e-02, -3.93389583e-01,
5.28795272e-02, 3.79496545e-01, 2.46415466e-01,
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Dense_2: {
bias: array([-1.9683748e-02, -2.1595210e-03, 6.7055225e-06, -5.0753772e-02,
1.3499202e-01, 4.5890406e-02, -1.5943155e-02, 6.7055225e-06],
dtype=float32),
kernel: array([[ 1.70742482e-01, -9.91618633e-02, -2.29330659e-02,
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},
Dense_3: {
bias: array([0.3012179], dtype=float32),
kernel: array([[-0.04792835],
[ 0.6472556 ],
[ 0.19396764],
[-0.19879013],
[ 0.4868285 ],
[ 0.70990056],
[-0.05229847],
[-0.20487049]], dtype=float32),
},
},
})
最后，让我们验证模型。
[8]:
X_test, y_test = breast_cancer(train=False)
auc = validate_model(params, X_test, y_test)
print(f'auc={auc}')
auc=0.9921388797903701
实验到此结束。