Pytorch: MultiMarginLoss has no attribute 'backward'

Works fine for me with (almost) the latest version ('0.1.11+8aa1cef')

import torch
import torch.nn as nn
from torch.autograd import Variable

y = Variable(torch.rand(5, 3), requires_grad=True)
t = Variable(torch.LongTensor(5).random_(0, 2))
m = nn.MultiMarginLoss()
loss = m(y, t)
loss.backward()
print(y.grad)

outputs

Variable containing:
-0.1333  0.0667  0.0667
 0.0667 -0.1333  0.0667
 0.0667 -0.1333  0.0667
 0.0667 -0.1333  0.0667
 0.0667 -0.1333  0.0667
[torch.FloatTensor of size 5x3]

Hi,
The nn.Module does not have a backward (none of them have), their forward is implemented with autograd compliant methods and is thus automatically differentiated.
If you want to find the implementation for MultiMarginLoss, it is implemented here in c.

Thanks. I'm just getting started with PyTorch. I understand it now.