Pin_memory() on variables?

Hi there,

this is a newbie question (sorry…). I’m trying to speed-up my GPU training. The docs recommend pin_memory(), which works with tensors, but not with variables (torch version 0.3.0):

import torch
from torch.autograd import Variable
dtype = torch.FloatTensor

my_tensor = torch.randn(10,10).type(dtype)
my_variable = Variable(torch.randn(10,10).type(dtype), requires_grad=False)

my_tensor.cuda() # -> works
my_tensor.pin_memory() # -> works

my_variable.cuda() # -> works
my_variable.pin_memory() # -> does not work

gives me the error

Traceback (most recent call last):
  File "", line 14, in <module>
    my_variable.pin_memory() # -> does not work
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/", line 67, in __getattr__
    return object.__getattribute__(self, name)
AttributeError: 'Variable' object has no attribute 'pin_memory'

I wonder why this is… As my_variable.cuda() allows me to send Variables to GPU (just like tensors), I would expect pin_memory() to work for Variables too.

  1. Is my understanding fundamentally wrong here?
  2. What’s the correct way of pinning memory (a) for all of a model’s variables/tensors, (b) when only pinning some of the variables? (A web search gives me only examples in combination with DataLoader(), which does not apply in my case).

Thanks a lot!

you can do for now. But do make sure you understand why you are pinning memory, easy to fall into a trap there. Pinning memory is only useful for CPU Tensors that have to be moved to the GPU. So, pinning all of a model’s variables/tensors doesn’t make sense at all.


Hi there,

thanks for the quick reply -

Pinning memory is only useful for CPU Tensors that have to be moved to the GPU.

OK, I understand now that this is about moving input data to the GPU.

But what about the model? Am I correct that the model (and its parameters + operations) reside / are carried out on the GPU anyway?

And if not: How can I taylor which operations to carry out on the GPU? (sorry if this is a stupid question, but I haven’t been able to dig up proper documentation for that…).

Yes, as long as you have model.cuda() somewhere.

Cool - thanks for the quick reply!