In PyTorch, the tensor’s memory layout can impact the computational efficiency. When elements are stored continuously in a block of memory, we call it a contiguous tensor. The continuous arrangement makes the iterating over the tensor element efficient.

### What is contiguous()?

The use of `.contiguous()`

the method in PyTorch is to ensure that memory is laid out in a contiguous manner. It returns a contiguous copy of the tensor, rearranging the data if they are already in a contiguous manner.

### When do we use contiguous()?

When we perform certain operations such as `transpose()`

or `view()`

or slicing the tensor may become non-contiguous and change the tensor’s shape, or the order of elements without modifying the underlying data in memory. This results in the loss of contiguous layout.

For example, consider a tensor `A`

and its transpose `A.T`

. While `A`

might be contiguous, `A.T`

is typically non-contiguous because the memory layout does not change during transpose – only the way it is indexed changes.

```
import torch
# Creating a tensor
A = torch.rand(3, 4)
# Transposing the tensor
B = A.transpose(0, 1)
# Checking if B is contiguous
print("Is B contiguous? ", B.is_contiguous())
# Making B contiguous
C = B.contiguous()
# Checking if C is now contiguous
print("Is C contiguous? ", C.is_contiguous())
```

In the above example, After applying the `transpose()`

, `B`

became non-contiguous. Using `.contiguous()`

, we create a contiguous tensor `C`

from `B`

.

### Frequently asked questions:

**Does**`.contiguous()`

always create a copy of the tensor?

No,`.contiguous()`

only creates a copy if the tensor is non-contiguous. Otherwise, it returns the original tensor.**How does non-contiguity affect tensor operations?**

Non-contiguous tensors can lead to inefficient memory access patterns, slowing down operations and computations.**Can**`.contiguous()`

affect the values in a tensor?

No,`.contiguous()`

does not change the tensor’s values; it only affects the memory layout.**Is it necessary to use**`.contiguous()`

after every operation like`transpose()`

?

It depends on your subsequent operations. If an operation requires contiguous tensors, then`.contiguous()`

should be used.**How do I check if a tensor is contiguous?**

Use the`is_contiguous()`

method on a tensor to check its contiguity.