# Reshaping PyTorch Tensors – DZone AI

It is a reasonable thing to expect n-dimensional tensor to have a possibility to be reshaped. Reshape means to change the spatial size of a container that holds underlying data. One can create any n-dimensional tensor that wraps a numerical array as long as the product of dimensions stays equal to the number of the array’s elements.

```
import torch
# underlying data
data = [1,2,3,4,5,6,7,8] # has 8 elements
# two ways to store identical data
tens_A = torch.tensor(data).reshape(shape=(2,4)) # 2-dimensional tensor of shape (2,4)
tens_B = torch.tensor(data).reshape(shape=(2,2,2)) # 3-dimensional tensor of shape (2,2,2)
```

The same holds for reshaping. It’s possible to convert the existing container to a different spatial size while it preserves the total number of elements.

```
import torch
data = [1,2,3,4,5,6,7,8]
# Original tensor of shape 2x4
tens_A = torch.tensor(data).reshape(shape=(2,4))
# Reshaped from 2x4 to 2x2x2 (preserving number of elements)
tens_B = tens_A.reshape(shape=(2,2,2))
```

This is quite intuitive. But, the experienced users might notice a couple of not obvious things.

- Will the reshaped tensor point to the same underlying data or will the data be copied?
- How to find where elements with known previously indices do live in a new container?

The answer to the first question is that reshaped tensor *sometimes triggers copying of underlying data* *and sometimes doesn’t*. The documentation says the following about reshape method:

Returns a tensor with the same data and number of elements as input, but with the specified shape. When possible, the returned tensor will be a

viewof input. Otherwise, it will be a copy.Contiguousinputs and inputs with compatiblestridescan be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

This quote introduces three things:** the view of a tensor**, the **stride of a tensor, **and** contiguous tensors**. Let’s talk about them in order.

## Stride

Stride is a tuple of integers each of which represents a jump needed to perform on the underlying data to go to the next element within the current dimension. The tensor’s underlying data is just a one-dimensional physical array, that is stored sequentially in the memory. If we have, for example, an arbitrary 3-dimensional container, then the elements at indices `(x,y,z)`

and at`(x+1,y,z)`

live at a distance of size `stride(0)`

within the underlying data. See the code below:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
# Start index
x = 0
y = 1
z = 3
# Translation from n-dimensional index to offset in the underlying data
offset = tens_A[x,y,z].storage_offset()
# The magnitude of a jump
jump = tens_A.stride(0) # or tens_A.stride()[0]
# Underlying 1d data
data = tens_A.storage()
# Add the jump to the offset of index (x,y,z)
# and compare against the element at index (x+1,y,z)
print(tens_A.storage()[offset + jump] == tens_A[x+1,y,z]) # it is True
```

Using all the stride entries we can translate any given n-dimensional index to the offset within a physical array by ourselves. Here’s the graphical interpretation of a stride. This image is cherry-picked from ezyang’s blog, one of the core PyTorch maintainers.

In the picture above, you can see how the index `(1,0)`

inside a 2-by-2 logical tensor translates to the offset of a value `2`

within a physical array using `strides`

.

**Contiguous Tensor**

First, consider a one-dimensional array. One might say it is contiguous if the entries are stored sequentially without any spaces in between. Visualizations from this StackOverflow answer will help us:

Then, consider a multi-dimensional array. A special case is two dimensions, 2d array is contiguous if the underlying data is stored row-wise sequentially.

Finally, we can say that a PyTorch tensor is contiguous if the n-dimensional array it represents is contiguous.

Remark. By “contiguous” we understand “C contiguous”, i.e. the way arrays are stored in language C. There’s also a “Fortran contiguous” alternative when the arrays are stored in column-wise order.

One can deduce the contiguity from the tensor strides. If we take an arbitrary n-dimensional tensor that is contiguous then each stride in the tuple is the product of the corresponding tail of tensor sizes. As in the example:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
sizes = tens_A.shape
print(tens_A.is_contiguous()) # outputs: True
print(tens_A.stride()) # outputs: (12,4,1)
print(tens_A.stride() == (sizes[1]*sizes[2], sizes[2], 1)) # outputs: True
```

## Loss and Restoration of Contiguity

Another interesting aspect is that some of the view operators return a new tensor object based on the same underlying data but treat it in a non-contiguous way. For example, transposition. But there’s a way of restoring contiguity using the method `contiguous`

on a tensor, which might trigger copying if the tensor was non-contiguous.

Contiguity is crucial when reshaping is performed. If the tensor is not contiguous then `reshape`

will silently trigger data copying which is not efficient but in some cases it’s unavoidable.

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
# Transposed tensor
transp_A = tens_A.t()
# Check contiguity
print(transp_A.is_contiguous()) # output: False
# Make contiguous
tens_B = transp_A.contiguous() # triggers copying because wasn't contiguous
print(tens_B.is_contiguous()) # output: True
# Reshaping
resh_transp_A = transp_A.reshape(shape=(2,2,2,3)) # triggers copying
resh_tens_B = tens_B.reshape(shape=(2,2,2,3)) # won't trigger copying
```

The code above shows the cases when the copying happens silently because of non-contiguous order.

## View

This method is aimed to reshape tensor efficiently, when possible. That means to use underlying data without explicit copying. The reshape itself is performed by updating the *shape *and *stride *of a tensor. The rule of thumb is the following the tensor could be reshaped by `view`

if it `is_contiguous==True`

.

Let’s have a look at this example:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
def test_view(tensor, sizes):
try:
tensor.view(*sizes)
except Exception as e:
print(e)
print(f"View was Failed: tensor.is_contiguos == {tensor.is_contiguous()}")
else:
print(f"View was Successful: tensor.is_contiguos == {tensor.is_contiguous()}")
sizes = (3,4,2)
# Let's try to use view
test_view(tens_A, sizes)
# Apply view-function that change contiguity and try again
perm_tens_A = tens_A.permute(0,2,1) # change order of axis
test_view(perm_tens_A, sizes) # this will result in RuntimeError("Use .reshape(...) instead")
```

Here are two tests. The one on contiguous tensor and the other on non-contiguous, it is seen that in the latter case view is impossible and it recommends us to use `reshape`

instead.

## Summarizing

Here it is! That much theory we need to correctly interpret the documentation on `reshape`

function. And we are ready to answer the first question stated at the beginning: does `reshape`

explicitly copy underlying data?

Reshape method applied on a tensor will try to invoke the `view`

method if it is possible, if not then the tensor data will be copied to be *contiguous*, i.e. to live in the memory sequentially and to have proper *strides*, and after this manipulation, it will invoke the `view`

.

Frankly speaking, I cannot fully understand documentation on view method. It is unclear for me what they mean by saying: “…size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions…” . If you have any ideas please tell in comments

## The Second Question

It is might be clear now that indexing within a reshaped tensor hardly relies on the *stride. *When we perform reshaping or view the tensor *shape* and *stride* properties change. This two are sufficient to deduce where the elements do live after the modification.

It is a reasonable thing to expect n-dimensional tensor to have a possibility to be reshaped. Reshape means to change the spatial size of a container that holds underlying data. One can create any n-dimensional tensor that wraps a numerical array as long as the product of dimensions stays equal to the number of the array’s elements.

```
import torch
# underlying data
data = [1,2,3,4,5,6,7,8] # has 8 elements
# two ways to store identical data
tens_A = torch.tensor(data).reshape(shape=(2,4)) # 2-dimensional tensor of shape (2,4)
tens_B = torch.tensor(data).reshape(shape=(2,2,2)) # 3-dimensional tensor of shape (2,2,2)
```

The same holds for reshaping. It’s possible to convert the existing container to a different spatial size while it preserves the total number of elements.

```
import torch
data = [1,2,3,4,5,6,7,8]
# Original tensor of shape 2x4
tens_A = torch.tensor(data).reshape(shape=(2,4))
# Reshaped from 2x4 to 2x2x2 (preserving number of elements)
tens_B = tens_A.reshape(shape=(2,2,2))
```

This is quite intuitive. But, the experienced users might notice a couple of not obvious things.

- Will the reshaped tensor point to the same underlying data or will the data be copied?
- How to find where elements with known previously indices do live in a new container?

The answer to the first question is that reshaped tensor *sometimes triggers copying of underlying data* *and sometimes doesn’t*. The documentation says the following about reshape method:

Returns a tensor with the same data and number of elements as input, but with the specified shape. When possible, the returned tensor will be a

viewof input. Otherwise, it will be a copy.Contiguousinputs and inputs with compatiblestridescan be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

This quote introduces three things:** the view of a tensor**, the **stride of a tensor, **and** contiguous tensors**. Let’s talk about them in order.

## Stride

Stride is a tuple of integers each of which represents a jump needed to perform on the underlying data to go to the next element within the current dimension. The tensor’s underlying data is just a one-dimensional physical array, that is stored sequentially in the memory. If we have, for example, an arbitrary 3-dimensional container, then the elements at indices `(x,y,z)`

and at`(x+1,y,z)`

live at a distance of size `stride(0)`

within the underlying data. See the code below:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
# Start index
x = 0
y = 1
z = 3
# Translation from n-dimensional index to offset in the underlying data
offset = tens_A[x,y,z].storage_offset()
# The magnitude of a jump
jump = tens_A.stride(0) # or tens_A.stride()[0]
# Underlying 1d data
data = tens_A.storage()
# Add the jump to the offset of index (x,y,z)
# and compare against the element at index (x+1,y,z)
print(tens_A.storage()[offset + jump] == tens_A[x+1,y,z]) # it is True
```

Using all the stride entries we can translate any given n-dimensional index to the offset within a physical array by ourselves. Here’s the graphical interpretation of a stride. This image is cherry-picked from ezyang’s blog, one of the core PyTorch maintainers.

In the picture above, you can see how the index `(1,0)`

inside a 2-by-2 logical tensor translates to the offset of a value `2`

within a physical array using `strides`

.

**Contiguous Tensor**

First, consider a one-dimensional array. One might say it is contiguous if the entries are stored sequentially without any spaces in between. Visualizations from this StackOverflow answer will help us:

Then, consider a multi-dimensional array. A special case is two dimensions, 2d array is contiguous if the underlying data is stored row-wise sequentially.

Finally, we can say that a PyTorch tensor is contiguous if the n-dimensional array it represents is contiguous.

Remark. By “contiguous” we understand “C contiguous”, i.e. the way arrays are stored in language C. There’s also a “Fortran contiguous” alternative when the arrays are stored in column-wise order.

One can deduce the contiguity from the tensor strides. If we take an arbitrary n-dimensional tensor that is contiguous then each stride in the tuple is the product of the corresponding tail of tensor sizes. As in the example:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
sizes = tens_A.shape
print(tens_A.is_contiguous()) # outputs: True
print(tens_A.stride()) # outputs: (12,4,1)
print(tens_A.stride() == (sizes[1]*sizes[2], sizes[2], 1)) # outputs: True
```

## Loss and Restoration of Contiguity

Another interesting aspect is that some of the view operators return a new tensor object based on the same underlying data but treat it in a non-contiguous way. For example, transposition. But there’s a way of restoring contiguity using the method `contiguous`

on a tensor, which might trigger copying if the tensor was non-contiguous.

Contiguity is crucial when reshaping is performed. If the tensor is not contiguous then `reshape`

will silently trigger data copying which is not efficient but in some cases it’s unavoidable.

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
# Transposed tensor
transp_A = tens_A.t()
# Check contiguity
print(transp_A.is_contiguous()) # output: False
# Make contiguous
tens_B = transp_A.contiguous() # triggers copying because wasn't contiguous
print(tens_B.is_contiguous()) # output: True
# Reshaping
resh_transp_A = transp_A.reshape(shape=(2,2,2,3)) # triggers copying
resh_tens_B = tens_B.reshape(shape=(2,2,2,3)) # won't trigger copying
```

The code above shows the cases when the copying happens silently because of non-contiguous order.

## View

This method is aimed to reshape tensor efficiently, when possible. That means to use underlying data without explicit copying. The reshape itself is performed by updating the *shape *and *stride *of a tensor. The rule of thumb is the following the tensor could be reshaped by `view`

if it `is_contiguous==True`

.

Let’s have a look at this example:

```
import torch
# Tensor of interest
tens_A = torch.rand((2,3,4)) # 3-dimensional tensor of shape (2,3,4)
def test_view(tensor, sizes):
try:
tensor.view(*sizes)
except Exception as e:
print(e)
print(f"View was Failed: tensor.is_contiguos == {tensor.is_contiguous()}")
else:
print(f"View was Successful: tensor.is_contiguos == {tensor.is_contiguous()}")
sizes = (3,4,2)
# Let's try to use view
test_view(tens_A, sizes)
# Apply view-function that change contiguity and try again
perm_tens_A = tens_A.permute(0,2,1) # change order of axis
test_view(perm_tens_A, sizes) # this will result in RuntimeError("Use .reshape(...) instead")
```

Here are two tests. The one on contiguous tensor and the other on non-contiguous, it is seen that in the latter case view is impossible and it recommends us to use `reshape`

instead.

## Summarizing

Here it is! That much theory we need to correctly interpret the documentation on `reshape`

function. And we are ready to answer the first question stated at the beginning: does `reshape`

explicitly copy underlying data?

Reshape method applied on a tensor will try to invoke the `view`

method if it is possible, if not then the tensor data will be copied to be *contiguous*, i.e. to live in the memory sequentially and to have proper *strides*, and after this manipulation, it will invoke the `view`

.

Frankly speaking, I cannot fully understand documentation on view method. It is unclear for me what they mean by saying: “…size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions…” . If you have any ideas please tell in comments

## The Second Question

It is might be clear now that indexing within a reshaped tensor hardly relies on the *stride. *When we perform reshaping or view the tensor *shape* and *stride* properties change. This two are sufficient to deduce where the elements do live after the modification.

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