PyTorch - contiguous()

Question

I was going through this example of a LSTM language model on github (link). What it does in general is pretty clear to me. But I\'m still struggling to understand what calli

Answer 1

There are few operations on Tensor in PyTorch that do not really change the content of the tensor, but only how to convert indices in to tensor to byte location. These operations include:

narrow(), view(), expand() and transpose()

For example: when you call transpose(), PyTorch doesn't generate new tensor with new layout, it just modifies meta information in Tensor object so offset and stride are for new shape. The transposed tensor and original tensor are indeed sharing the memory!

x = torch.randn(3,2)
y = torch.transpose(x, 0, 1)
x[0, 0] = 42
print(y[0,0])
# prints 42

This is where the concept of contiguous comes in. Above x is contiguous but y is not because its memory layout is different than a tensor of same shape made from scratch. Note that the word "contiguous" is bit misleading because its not that the content of tensor is spread out around disconnected blocks of memory. Here bytes are still allocated in one block of memory but the order of the elements is different!

When you call contiguous(), it actually makes a copy of tensor so the order of elements would be same as if tensor of same shape created from scratch.

Normally you don't need to worry about this. If PyTorch expects contiguous tensor but if its not then you will get RuntimeError: input is not contiguous and then you just add a call to contiguous().