Deep-Learning-with-PyTorch

62 CHAPTER 3 It starts with a tensor
# In[46]:
points_t_cont.storage()
# Out[46]:
4.0
5.0
2.0
1.0
3.0
1.0
[torch.FloatStorage of size 6]
Notice that the storage has been reshuffled in order for elements to be laid out rowby-row
in the new storage. The stride has been changed to reflect the new layout.
As a refresher, figure 3.7 shows our diagram again. Hopefully it will all make sense
now that we’ve taken a good look at how tensors are built.
5 7 4
1 3 2
7 3 8
OFfSET = 1
SHAPE = (3, 3)
ROWS
(first INDEX)
STRIDE = (3, 1)
COLS
(second INDEX)
+1 NEXT COL (STRIDE[1]=1)
6 5 7 4 1 3 2 7 3 8
+3 NEXT ROW (STRIDE[0]=3)
Figure 3.7 Relationship between a tensor’s offset, size, and stride. Here the tensor is a view
of a larger storage, like one that might have been allocated when creating a larger tensor.
3.9 Moving tensors to the GPU
So far in this chapter, when we’ve talked about storage, we’ve meant memory on the
CPU. PyTorch tensors also can be stored on a different kind of processor: a graphics
processing unit (GPU). Every PyTorch tensor can be transferred to (one of) the
GPU(s) in order to perform massively parallel, fast computations. All operations that
will be performed on the tensor will be carried out using GPU-specific routines that
come with PyTorch.