Sorry. The comparison is between 0.2 and 0.4. Even with simple code like the one below the difference in memory footprint is large:
On the code below, 0.4.0a0+02b758f leaves a 952mb footprint, whereas 0.2 takes 369mb…
import torch
net = torch.nn.Conv1d(512, 512, kernel_size=(5,), stride=1, padding=2).cuda()
data = torch.autograd.Variable(torch.FloatTensor(1, 512, 107)).cuda()
out = net(data)

What would be the best strategy to produce the small reproducing example? The model in question is a seq2seq with attention and I’m not clear what parts can be removed such that the code is simple but the OOM remains…