Problem with narrowed tensor and

I noticed that when is used on a narrowed version of a tensor, it saves the whole tensor, but it returns the narrowed tensor. To me, this looks like a bug because it saves something more than what it is intended, and we are not able to recover this redundant info. I wanted to discuss this issue before reporting it as a bug. For that, I wrote a simple example, and you can check the output to see what I mean:

import torch
import os

x= torch.randint(0, 100, size=(10000,8), dtype=torch.long)

y1 = x.narrow(dim=0, start=0, length=10){"y": y1},"y1.pth")

y2 = x.narrow(dim=0, start=0, length=10).detach().clone(){"y": y2},"y2.pth")

loaded_y1 = torch.load("y1.pth")["y"]
loaded_y2 = torch.load("y2.pth")["y"]

if torch.equal(loaded_y1, loaded_y2):

if torch.equal(loaded_y1, y1):

print("size y1.pth (in Bytes)=", os.stat("y1.pth").st_size)
print("size y2.pth (in Bytes)=", os.stat("y2.pth").st_size)

This is the output of the above script:

size y1.pth (in Bytes)= 640754
size y2.pth (in Bytes)= 1394

As you can see, both “y1.pth” and “y2.pth” are equal, but “y1.pth” is much larger because it saves the mother tensor, while we are not able to recover it.

That seems indeed like a bug. Could you create an issue on GitHub so that we could track and fix it, please?

Yes sure. Thank you very much for your support.