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fedavg_tests.py
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fedavg_tests.py
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import asyncio
import copy
import unittest
import numpy as np
import torch
import os
os.environ["config_file"] = "tests/TestsConfig/fedavg_tests.yml"
from plato.clients import simple
from plato.algorithms import registry as algorithms_registry
from plato.servers import fedavg as fedavg_server
from plato.trainers import basic
from plato.config import Config
class InnerProductModel(torch.nn.Module):
def __init__(self, n: int = 10):
super().__init__()
self.layer = torch.nn.Linear(n, 1, bias=False)
self.layer.weight.data = torch.arange(n, dtype=torch.float32)
self.head = torch.nn.Linear(1, 1, bias=False)
self.head.weight.data = torch.arange(1, 2, dtype=torch.float32)
@staticmethod
def is_valid_model_type(model_type):
raise NotImplementedError
@staticmethod
def get_model_from_type(model_type):
raise NotImplementedError
@property
def loss_criterion(self):
return torch.nn.MSELoss()
def forward(self, x):
return self.layer(x)
async def test_fedavg_aggregation(self):
"""Testing the federated averaging implementation."""
print("\nTesting federated averaging.")
updates = []
model = InnerProductModel
trainer = basic.Trainer
algorithm = algorithms_registry.registered_algorithms[Config().algorithm.type]
server = fedavg_server.Server(model=model, algorithm=algorithm, trainer=trainer)
server.init_trainer()
weights = copy.deepcopy(self.algorithm.extract_weights())
print(f"Report 1 weights: {weights}")
updates.append(
simple.SimpleNamespace(
client_id=1,
report=simple.SimpleNamespace(
client_id=1,
num_samples=100,
accuracy=0,
training_time=0,
comm_time=0,
update_response=False,
),
payload=weights,
staleness=0,
)
)
self.trainer.model.train()
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.trainer.model.head.weight.data -= 0.1
self.assertEqual(44.0, self.trainer.model(self.example).item())
weights = copy.deepcopy(self.algorithm.extract_weights())
print(f"Report 2 weights: {weights}")
updates.append(
simple.SimpleNamespace(
client_id=2,
report=simple.SimpleNamespace(
client_id=2,
num_samples=100,
accuracy=0,
training_time=0,
comm_time=0,
update_response=False,
),
payload=weights,
staleness=0,
)
)
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.trainer.model.head.weight.data -= 0.1
self.assertEqual(43.2, np.round(self.trainer.model(self.example).item(), 4))
weights = copy.deepcopy(self.algorithm.extract_weights())
print(f"Report 3 Weights: {weights}")
updates.append(
simple.SimpleNamespace(
client_id=3,
report=simple.SimpleNamespace(
client_id=3,
num_samples=100,
accuracy=0,
training_time=0,
comm_time=0,
update_response=False,
),
payload=weights,
staleness=0,
)
)
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.trainer.model.head.weight.data -= 0.1
self.assertEqual(42.56, np.round(self.trainer.model(self.example).item(), 4))
weights = copy.deepcopy(self.algorithm.extract_weights())
print(f"Report 4 Weights: {weights}")
updates.append(
simple.SimpleNamespace(
client_id=4,
report=simple.SimpleNamespace(
client_id=4,
num_samples=100,
accuracy=0,
training_time=0,
comm_time=0,
update_response=False,
),
payload=weights,
staleness=0,
)
)
print(
f"Weights of the layer before federated averaging: {server.trainer.model.layer.weight.data}"
)
print(
f"Weights of the head before federated averaging: {server.trainer.model.head.weight.data}"
)
weights_received = [update.payload for update in updates]
baseline_weights = server.algorithm.extract_weights()
deltas_received = server.algorithm.compute_weight_deltas(
baseline_weights, weights_received
)
deltas = await server.aggregate_deltas(updates, deltas_received)
updated_weights = server.algorithm.update_weights(deltas)
server.algorithm.load_weights(updated_weights)
print(
f"Weights of the layer after federated averaging: {server.trainer.model.layer.weight.data}"
)
print(
f"Weights of the head after federated averaging: {server.trainer.model.head.weight.data}"
)
self.assertEqual(42.56, np.round(self.trainer.model(self.example).item(), 4))
class FedAvgTest(unittest.TestCase):
def setUp(self):
super().setUp()
self.model = InnerProductModel
self.example = torch.ones(1, 10)
self.label = torch.ones(1) * 40.0
self.trainer = basic.Trainer(model=self.model)
self.algorithm = algorithms_registry.get(trainer=self.trainer)
self.optimizer = torch.optim.SGD(self.trainer.model.parameters(), lr=0.01)
def test_forward(self):
self.assertIsNotNone(self.model)
weights = self.algorithm.extract_weights()
print("\nTesting forward pass.")
print(f"Weights: {weights}")
self.assertEqual(45.0, self.trainer.model(self.example).item())
def test_backward(self):
print("\nTesting backward pass.")
self.trainer.model.train()
if hasattr(self.algorithm, "extract_submodules_name"):
print(
"\nTesting submodules extraction.",
self.algorithm.extract_submodules_name(
self.trainer.model.state_dict().keys()
),
)
if hasattr(self.algorithm, "is_consistent_weights"):
print(
"\nTesting weights consistency judge.",
self.algorithm.is_consistent_weights(
self.trainer.model.state_dict().keys()
),
)
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.assertEqual(44.0, self.trainer.model(self.example).item())
weights = self.algorithm.extract_weights()
print(f"Weights: {weights}")
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.assertEqual(43.2, np.round(self.trainer.model(self.example).item(), 4))
weights = self.algorithm.extract_weights()
print(f"Weights: {weights}")
self.optimizer.zero_grad()
self.trainer.model.loss_criterion(
self.trainer.model(self.example), self.label
).backward()
self.optimizer.step()
self.assertEqual(42.56, np.round(self.trainer.model(self.example).item(), 4))
weights = self.algorithm.extract_weights()
print(f"Weights: {weights}")
def test_fedavg_aggregation(self):
asyncio.run(test_fedavg_aggregation(self))
if __name__ == "__main__":
unittest.main()