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update.py
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update.py
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import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
import copy
def repackage_hidden(h):
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
def get_batch(source, i):
seq_len = min(35, len(source) - 1 - i)
data = source[i:i + seq_len]
target = source[i + 1:i + 1 + seq_len].view(-1)
return data.to('cuda'), target.to('cuda')
class DatasetSplit(Dataset):
def __init__(self, dataset, idxs):
self.dataset = dataset
self.idxs = [int(i) for i in idxs]
def __len__(self):
return len(self.idxs)
def __getitem__(self, item):
image, label = self.dataset[self.idxs[item]]
return torch.tensor(image), torch.tensor(label)
class LocalUpdate(object):
def __init__(self, gpu, dataset, idxs, local_bs, dataset_name):
if dataset_name != 'wiki':
self.trainloader, self.validloader, self.testloader = self.train_val_test(local_bs, dataset, list(idxs))
self.device = 'cuda' if gpu else 'cpu'
self.dataset_name = dataset_name
if dataset_name == 'wiki':
self.criterion = nn.NLLLoss().to(self.device)
else:
self.criterion = nn.CrossEntropyLoss().to(self.device)
self.idxs = idxs
self.local_bs = local_bs
self.prev_model = None
self.last_round = None
self.layer_changes = dict()
def train_val_test(self, local_bs, dataset, idxs):
idxs_train = idxs[:int(0.8 * len(idxs))]
idxs_val = idxs[int(0.8 * len(idxs)):int(0.9 * len(idxs))]
idxs_test = idxs[int(0.9 * len(idxs)):]
trainloader = DataLoader(DatasetSplit(dataset, idxs_train),
batch_size=local_bs, shuffle=True)
validloader = DataLoader(DatasetSplit(dataset, idxs_val),
batch_size=int(len(idxs_val) / 1), shuffle=False)
testloader = DataLoader(DatasetSplit(dataset, idxs_test),
batch_size=int(len(idxs_test) / 1), shuffle=False)
return trainloader, validloader, testloader
def update_weights(self, model, global_round, optimizer, lr, local_ep):
self.prev_model = copy.deepcopy(model.state_dict())
self.last_round = global_round
model.train()
epoch_loss = []
if optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(), lr=lr,
momentum=0.5)
elif optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=lr,
weight_decay=1e-4)
for _ in range(local_ep):
batch_loss = []
if self.dataset_name == 'wiki':
hidden = model.init_hidden(20)
for batch, i in enumerate(range(0, self.idxs.size(0) - 1, 35)):
data, targets = get_batch(self.idxs, i)
model.zero_grad()
hidden = repackage_hidden(hidden)
output, hidden = model(data, hidden)
loss = self.criterion(output, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.25)
for p in model.parameters():
p.data.add_(p.grad, alpha=-20)
batch_loss.append(loss.item())
epoch_loss.append(sum(batch_loss) / len(batch_loss))
else:
for _, (images, labels) in enumerate(self.trainloader):
images, labels = images.to(self.device), labels.to(self.device)
model.zero_grad()
log_probs = model(images)
loss = self.criterion(log_probs, labels)
loss.backward()
optimizer.step()
batch_loss.append(loss.item())
epoch_loss.append(sum(batch_loss) / len(batch_loss))
delta_model = self.parameter_delta_weights(copy.deepcopy(model.state_dict()))
model.load_state_dict(delta_model)
return model, sum(epoch_loss) / len(epoch_loss)
def parameter_delta_weights(self, latest_local_model):
w_delta = latest_local_model
for key in w_delta.keys():
w_delta[key] = torch.subtract(w_delta[key], self.prev_model[key])
return w_delta
@torch.no_grad()
def inference(self, model):
model.eval()
loss, total, correct = 0.0, 0.0, 0.0
for batch_idx, (images, labels) in enumerate(self.testloader):
images, labels = images.to(self.device), labels.to(self.device)
outputs = model(images)
batch_loss = self.criterion(outputs, labels)
loss += batch_loss.item()
_, pred_labels = torch.max(outputs, 1)
pred_labels = pred_labels.view(-1)
correct += torch.sum(torch.eq(pred_labels, labels)).item()
total += len(labels)
accuracy = correct / total
return accuracy, loss
@torch.no_grad()
def test_inference(gpu, model, test_dataset, dataset):
model.eval()
loss, total, correct = 0.0, 0.0, 0.0
device = 'cuda' if gpu else 'cpu'
if dataset == "wiki":
criterion = torch.nn.NLLLoss().to(device)
hidden = model.init_hidden(20)
total_loss = 0.
for batch, i in enumerate(range(0, test_dataset.size(0) - 1, 35)):
data, targets = get_batch(test_dataset, i)
output, hidden = model(data, hidden)
hidden = repackage_hidden(hidden)
total_loss += len(data) * criterion(output, targets).item()
return total_loss / (len(test_dataset) - 1)
else:
criterion = torch.nn.CrossEntropyLoss().to(device)
testloader = DataLoader(test_dataset, batch_size=128, shuffle=False)
for batch_idx, (images, labels) in enumerate(testloader):
images, labels = images.to(device), labels.to(device)
outputs = model(images)
batch_loss = criterion(outputs, labels)
loss += batch_loss.item()
_, pred_labels = torch.max(outputs, 1)
pred_labels = pred_labels.view(-1)
correct += torch.sum(torch.eq(pred_labels, labels)).item()
total += len(labels)
accuracy = correct / total
return accuracy, loss / len(testloader)