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@genekogan
Created October 23, 2019 04:36
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(w.i.p.) Federated variational autoencoder with PySyft
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
from torch.autograd import Variable
import syft as sy
# bob & alice
hook = sy.TorchHook(torch)
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
kwargs = {'num_workers': 1, 'pin_memory': True} if torch.cuda.is_available() else {}
# model & training parameters
image_size = 784
h_dim = 400
z_dim = 20
num_epochs = 10
batch_size = 64
learning_rate = 1e-3
# data loader
federated_train_loader = sy.FederatedDataLoader( # <-- this is now a FederatedDataLoader
torchvision.datasets.MNIST(root='../data',
train=True, download=True, transform=transforms.ToTensor())
.federate((bob, alice)),
batch_size=batch_size, shuffle=True, **kwargs)
class VAE(nn.Module):
def __init__(self, image_size=784, h_dim=400, z_dim=20):
super(VAE, self).__init__()
self.fc1 = nn.Linear(image_size, h_dim)
self.fc2 = nn.Linear(h_dim, z_dim)
self.fc3 = nn.Linear(h_dim, z_dim)
self.fc4 = nn.Linear(z_dim, h_dim)
self.fc5 = nn.Linear(h_dim, image_size)
def encode(self, x):
h = F.relu(self.fc1(x))
return self.fc2(h), self.fc3(h)
def reparameterize(self, mu, log_var):
std = torch.exp(log_var/2)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h = F.relu(self.fc4(z))
return F.sigmoid(self.fc5(h))
def forward(self, x):
mu, log_var = self.encode(x)
z = self.reparameterize(mu, log_var)
x_reconst = self.decode(z)
return x_reconst, mu, log_var
model = VAE().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
for batch_idx, (data, target) in enumerate(federated_train_loader):
model.send(data.location)
data, target = data.to(device).view(-1, image_size), target.to(device)
x_reconst, mu, log_var = model(data)
reconst_loss = F.binary_cross_entropy(x_reconst, data, size_average=False)
kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
# backward
loss = reconst_loss + kl_div
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.get()
if (batch_idx+1) % 10 == 0:
print ("Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}"
.format(epoch+1, num_epochs, batch_idx+1, len(federated_train_loader), reconst_loss.get(), kl_div.get()))
@genekogan
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Issue: Using randn_like in the model results a TensorsNotCollocatedException at line 52 because eps = torch.randn_like(std) is unexpectedly returned as a local tensor rather than a pointer to the worker machine. This would be fixed by removing randn_like from the list of torch functions excluded from the syft hook.

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