def prepare_training(hook, alice): # pragma: no cover
data, target = utils.create_gaussian_mixture_toy_data(nr_samples=100)
dataset_key = "gaussian_mixture"
dataset = sy.BaseDataset(data, target)
alice.add_dataset(dataset, key=dataset_key)
@hook.torch.jit.script
def loss_fn(pred, target):
return ((target.float() - pred.float())**2).mean()
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2, 3)
self.fc2 = nn.Linear(3, 2)
self.fc3 = nn.Linear(2, 1)
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.xavier_uniform_(self.fc2.weight)
nn.init.xavier_uniform_(self.fc3.weight)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, data)
pred = model(data)
loss_before = loss_fn(target=target, pred=pred)
return model, loss_fn, data, target, loss_before, dataset_key
def test_train_config_with_jit_trace_sync(
hook, start_remote_worker): # pragma: no cover
data, target = utils.create_gaussian_mixture_toy_data(100)
dataset = sy.BaseDataset(data, target)
dataset_key = "gaussian_mixture"
server, remote_proxy = start_remote_worker(id="sync_fit",
hook=hook,
port=9000,
dataset=(dataset, dataset_key))
@hook.torch.jit.script
def loss_fn(pred, target):
return ((target.view(pred.shape).float() - pred.float())**2).mean()
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2, 3)
self.fc2 = nn.Linear(3, 2)
self.fc3 = nn.Linear(2, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, data)
pred = model(data)
loss_before = loss_fn(pred=pred, target=target)
# Create and send train config
train_config = sy.TrainConfig(model=model,
loss_fn=loss_fn,
batch_size=2,
epochs=1)
train_config.send(remote_proxy)
for epoch in range(5):
loss = remote_proxy.fit(dataset_key=dataset_key)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("-" * 50)
print("Iteration %s: alice's loss: %s" % (epoch, loss))
new_model = train_config.model_ptr.get()
# assert that the new model has updated (modified) parameters
assert not ((model.fc1._parameters["weight"] -
new_model.obj.fc1._parameters["weight"]).abs() < 10e-3).all()
assert not ((model.fc2._parameters["weight"] -
new_model.obj.fc2._parameters["weight"]).abs() < 10e-3).all()
assert not ((model.fc3._parameters["weight"] -
new_model.obj.fc3._parameters["weight"]).abs() < 10e-3).all()
assert not ((model.fc1._parameters["bias"] -
new_model.obj.fc1._parameters["bias"]).abs() < 10e-3).all()
assert not ((model.fc2._parameters["bias"] -
new_model.obj.fc2._parameters["bias"]).abs() < 10e-3).all()
assert not ((model.fc3._parameters["bias"] -
new_model.obj.fc3._parameters["bias"]).abs() < 10e-3).all()
new_model.obj.eval()
pred = new_model.obj(data)
loss_after = loss_fn(pred=pred, target=target)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
print("Loss after training: {}".format(loss_after))
remote_proxy.close()
server.terminate()
assert loss_after < loss_before
async def test_train_config_with_jit_trace_async(
hook, start_proc): # pragma: no cover
kwargs = {
"id": "async_fit",
"host": "localhost",
"port": 8777,
"hook": hook
}
# data = torch.tensor([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0], [0.0, 0.0]], requires_grad=True)
# target = torch.tensor([[1.0], [1.0], [0.0], [0.0]], requires_grad=False)
# dataset_key = "xor"
data, target = utils.create_gaussian_mixture_toy_data(100)
dataset_key = "gaussian_mixture"
mock_data = torch.zeros(1, 2)
# TODO check reason for error (RuntimeError: This event loop is already running) when starting websocket server from pytest-asyncio environment
# dataset = sy.BaseDataset(data, target)
# server, remote_proxy = start_remote_worker(id="async_fit", port=8777, hook=hook, dataset=(dataset, dataset_key))
# time.sleep(0.1)
remote_proxy = WebsocketClientWorker(**kwargs)
@hook.torch.jit.script
def loss_fn(pred, target):
return ((target.view(pred.shape).float() - pred.float())**2).mean()
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2, 3)
self.fc2 = nn.Linear(3, 2)
self.fc3 = nn.Linear(2, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, mock_data)
pred = model(data)
loss_before = loss_fn(target=target, pred=pred)
# Create and send train config
train_config = sy.TrainConfig(model=model,
loss_fn=loss_fn,
batch_size=2,
optimizer="SGD",
optimizer_args={"lr": 0.1})
train_config.send(remote_proxy)
for epoch in range(5):
loss = await remote_proxy.async_fit(dataset_key=dataset_key)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("-" * 50)
print("Iteration %s: alice's loss: %s" % (epoch, loss))
new_model = train_config.model_ptr.get()
assert not (model.fc1._parameters["weight"]
== new_model.obj.fc1._parameters["weight"]).all()
assert not (model.fc2._parameters["weight"]
== new_model.obj.fc2._parameters["weight"]).all()
assert not (model.fc3._parameters["weight"]
== new_model.obj.fc3._parameters["weight"]).all()
assert not (model.fc1._parameters["bias"]
== new_model.obj.fc1._parameters["bias"]).all()
assert not (model.fc2._parameters["bias"]
== new_model.obj.fc2._parameters["bias"]).all()
assert not (model.fc3._parameters["bias"]
== new_model.obj.fc3._parameters["bias"]).all()
new_model.obj.eval()
pred = new_model.obj(data)
loss_after = loss_fn(target=target, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
print("Loss after training: {}".format(loss_after))
remote_proxy.close()
# server.terminate()
assert loss_after < loss_before
def start_websocket_server_worker(
id, host, port, hook, verbose, keep_labels=None, training=True
): # pragma: no cover
"""Helper function for spinning up a websocket server and setting up the local datasets."""
server = WebsocketServerWorker(id=id, host=host, port=port, hook=hook, verbose=verbose)
# Setup toy data (mnist example)
mnist_dataset = datasets.MNIST(
root="./data",
train=training,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
),
)
if training:
indices = np.isin(mnist_dataset.targets, keep_labels).astype("uint8")
logger.info("number of true indices: %s", indices.sum())
selected_data = (
torch.native_masked_select(mnist_dataset.data.transpose(0, 2), torch.tensor(indices))
.view(28, 28, -1)
.transpose(2, 0)
)
logger.info("after selection: %s", selected_data.shape)
selected_targets = torch.native_masked_select(mnist_dataset.targets, torch.tensor(indices))
dataset = sy.BaseDataset(
data=selected_data, targets=selected_targets, transform=mnist_dataset.transform
)
key = "mnist"
else:
dataset = sy.BaseDataset(
data=mnist_dataset.data,
targets=mnist_dataset.targets,
transform=mnist_dataset.transform,
)
key = "mnist_testing"
server.add_dataset(dataset, key=key)
# Setup toy data (vectors example)
data_vectors = torch.tensor([[-1, 2.0], [0, 1.1], [-1, 2.1], [0, 1.2]], requires_grad=True)
target_vectors = torch.tensor([[1], [0], [1], [0]])
server.add_dataset(sy.BaseDataset(data_vectors, target_vectors), key="vectors")
# Setup toy data (xor example)
data_xor = torch.tensor([[0.0, 1.0], [1.0, 0.0], [1.0, 1.0], [0.0, 0.0]], requires_grad=True)
target_xor = torch.tensor([1.0, 1.0, 0.0, 0.0], requires_grad=False)
server.add_dataset(sy.BaseDataset(data_xor, target_xor), key="xor")
# Setup gaussian mixture dataset
data, target = utils.create_gaussian_mixture_toy_data(nr_samples=100)
server.add_dataset(sy.BaseDataset(data, target), key="gaussian_mixture")
# Setup partial iris dataset
data, target = utils.iris_data_partial()
dataset = sy.BaseDataset(data, target)
dataset_key = "iris"
server.add_dataset(dataset, key=dataset_key)
logger.info("datasets: %s", server.datasets)
if training:
logger.info("len(datasets[mnist]): %s", len(server.datasets["mnist"]))
server.start()
return server
def test_fit():
data, target = utils.create_gaussian_mixture_toy_data(nr_samples=100)
fed_client = federated.FederatedClient()
dataset = sy.BaseDataset(data, target)
dataset_key = "gaussian_mixture"
fed_client.add_dataset(dataset, key=dataset_key)
def loss_fn(target, pred):
return torch.nn.functional.cross_entropy(input=pred, target=target)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(2, 3)
self.fc2 = torch.nn.Linear(3, 2)
torch.nn.init.xavier_normal_(self.fc1.weight)
torch.nn.init.xavier_normal_(self.fc2.weight)
def forward(self, x):
x = torch.nn.functional.relu(self.fc1(x))
x = torch.nn.functional.relu(self.fc2(x))
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, data)
model_id = 0
model_ow = pointers.ObjectWrapper(obj=model, id=model_id)
loss_id = 1
loss_ow = pointers.ObjectWrapper(obj=loss_fn, id=loss_id)
pred = model(data)
loss_before = loss_fn(target=target, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
# Create and send train config
train_config = sy.TrainConfig(
batch_size=8,
model=None,
loss_fn=None,
model_id=model_id,
loss_fn_id=loss_id,
lr=0.05,
weight_decay=0.01,
)
fed_client.set_obj(model_ow)
fed_client.set_obj(loss_ow)
fed_client.set_obj(train_config)
fed_client.optimizer = None
for curr_round in range(12):
loss = fed_client.fit(dataset_key=dataset_key)
if PRINT_IN_UNITTESTS and curr_round % 4 == 0: # pragma: no cover
print("-" * 50)
print("Iteration %s: alice's loss: %s" % (curr_round, loss))
new_model = fed_client.get_obj(model_id)
pred = new_model.obj(data)
loss_after = loss_fn(target=target, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover:
print("Loss after training: {}".format(loss_after))
assert loss_after < loss_before
def test_fit(fit_dataset_key, epochs):
data, target = utils.create_gaussian_mixture_toy_data(nr_samples=100)
fed_client = federated.FederatedClient()
dataset = sy.BaseDataset(data, target)
dataset_key = "gaussian_mixture"
fed_client.add_dataset(dataset, key=dataset_key)
def loss_fn(target, pred):
return torch.nn.functional.cross_entropy(input=pred, target=target)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(2, 3)
self.fc2 = torch.nn.Linear(3, 2)
torch.nn.init.xavier_normal_(self.fc1.weight)
torch.nn.init.xavier_normal_(self.fc2.weight)
def forward(self, x):
x = torch.nn.functional.relu(self.fc1(x))
x = torch.nn.functional.relu(self.fc2(x))
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, data)
model_id = 0
model_ow = pointers.ObjectWrapper(obj=model, id=model_id)
loss_id = 1
loss_ow = pointers.ObjectWrapper(obj=loss_fn, id=loss_id)
pred = model(data)
loss_before = loss_fn(target=target, pred=pred)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
# Create and send train config
train_config = sy.TrainConfig(
batch_size=8,
model=None,
loss_fn=None,
model_id=model_id,
loss_fn_id=loss_id,
optimizer_args={
"lr": 0.05,
"weight_decay": 0.01
},
epochs=epochs,
)
fed_client.set_obj(model_ow)
fed_client.set_obj(loss_ow)
fed_client.set_obj(train_config)
fed_client.optimizer = None
train_model(fed_client,
fit_dataset_key,
available_dataset_key=dataset_key,
nr_rounds=3)
if dataset_key == fit_dataset_key:
loss_after = evaluate_model(fed_client, model_id, loss_fn, data,
target)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss after training: {}".format(loss_after))
if loss_after >= loss_before: # pragma: no cover
if PRINT_IN_UNITTESTS:
print("Loss not reduced, train more: {}".format(loss_after))
train_model(fed_client,
fit_dataset_key,
available_dataset_key=dataset_key,
nr_rounds=10)
loss_after = evaluate_model(fed_client, model_id, loss_fn, data,
target)
assert loss_after < loss_before
def test_train_config_with_jit_trace_sync(hook,
start_proc): # pragma: no cover
kwargs = {
"id": "sync_fit",
"host": "localhost",
"port": 9000,
"hook": hook
}
# data = torch.tensor([[-1, 2.0], [0, 1.1], [-1, 2.1], [0, 1.2]], requires_grad=True)
# target = torch.tensor([[1], [0], [1], [0]])
data, target = utils.create_gaussian_mixture_toy_data(100)
dataset = sy.BaseDataset(data, target)
dataset_key = "gaussian_mixture"
process_remote_worker = start_proc(WebsocketServerWorker,
dataset=(dataset, dataset_key),
**kwargs)
time.sleep(0.1)
local_worker = WebsocketClientWorker(**kwargs)
@hook.torch.jit.script
def loss_fn(pred, target):
return ((target.view(pred.shape).float() - pred.float())**2).mean()
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2, 3)
self.fc2 = nn.Linear(3, 2)
self.fc3 = nn.Linear(2, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
model_untraced = Net()
model = torch.jit.trace(model_untraced, data)
pred = model(data)
loss_before = loss_fn(pred=pred, target=target)
# Create and send train config
train_config = sy.TrainConfig(model=model,
loss_fn=loss_fn,
batch_size=2,
epochs=1)
train_config.send(local_worker)
for epoch in range(5):
loss = local_worker.fit(dataset_key=dataset_key)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("-" * 50)
print("Iteration %s: alice's loss: %s" % (epoch, loss))
new_model = train_config.model_ptr.get()
# assert that the new model has updated (modified) parameters
assert not (model.fc1._parameters["weight"]
== new_model.obj.fc1._parameters["weight"]).all()
assert not (model.fc2._parameters["weight"]
== new_model.obj.fc2._parameters["weight"]).all()
assert not (model.fc3._parameters["weight"]
== new_model.obj.fc3._parameters["weight"]).all()
assert not (model.fc1._parameters["bias"]
== new_model.obj.fc1._parameters["bias"]).all()
assert not (model.fc2._parameters["bias"]
== new_model.obj.fc2._parameters["bias"]).all()
assert not (model.fc3._parameters["bias"]
== new_model.obj.fc3._parameters["bias"]).all()
new_model.obj.eval()
pred = new_model.obj(data)
loss_after = loss_fn(pred=pred, target=target)
if PRINT_IN_UNITTESTS: # pragma: no cover
print("Loss before training: {}".format(loss_before))
print("Loss after training: {}".format(loss_after))
local_worker.ws.shutdown()
del local_worker
time.sleep(0.1)
process_remote_worker.terminate()
assert loss_after < loss_before
