Working with Non-Contiguous Tensors in PyTorch: Best Practices and Alternatives

Contiguous vs. Non-Contiguous Memory in PyTorch Tensors

In PyTorch, a tensor's memory layout is considered contiguous if its elements are stored sequentially in memory, one after the other, without any gaps or jumps. This efficient memory arrangement allows for faster access and operations on the tensor's data.

However, there are scenarios where a tensor might have non-contiguous memory. This means its elements are scattered in non-sequential locations, potentially impacting performance. Here's what can lead to non-contiguous memory:

• import torch
  
  x = torch.tensor([[1, 2, 3], [4, 5, 6]])
  y = x.transpose(0, 1)  # y is a view with non-contiguous memory
  

• z = x[::2, :]  # Non-contiguous view with stride 2 in first dimension
  

Why Contiguous Memory Matters

While non-contiguous memory might not always be a performance bottleneck, it can affect the efficiency of certain operations, especially on GPUs. Contiguous tensors are generally preferred for:

• Faster computations: GPUs rely on coalesced memory access, where contiguous data allows for fetching multiple elements in a single operation. Non-contiguous access can lead to scattered reads and writes, impacting performance.
• Lower memory overhead: Contiguous memory might be more compact, reducing memory usage compared to non-contiguous layouts.

Checking for Contiguity and Making Tensors Contiguous

You can use the is_contiguous() method to check if a tensor has contiguous memory:

if x.is_contiguous():
    print("x is contiguous")
else:
    print("x is non-contiguous")

To create a contiguous copy of a non-contiguous tensor, use the contiguous() method:

contiguous_x = x.contiguous()  # Creates a new contiguous copy of x

Key Points to Remember

• Understand the distinction between contiguous and non-contiguous memory in PyTorch tensors.
• Be aware of operations that can create non-contiguous views.
• Check for contiguity when performance is critical, especially on GPUs.
• Use contiguous() to create a contiguous copy if necessary.

Transposing:

import torch

# Create a contiguous tensor
x = torch.tensor([[1, 2, 3], [4, 5, 6]])

# Check contiguity
print("x is contiguous:", x.is_contiguous())  # Output: True

# Create a non-contiguous view by transposing
y = x.transpose(0, 1)

# Check contiguity of the view
print("y (transposed) is contiguous:", y.is_contiguous())  # Output: False

# Access elements (optional)
print("x[0][1]:", x[0][1])  # Accessing x directly (contiguous)
# print("y[1][0]:", y[1][0])  # This might throw an error due to non-contiguous access pattern

Slicing with Strides:

# Create a contiguous tensor
x = torch.tensor(range(12)).reshape(3, 4)

# Check contiguity
print("x is contiguous:", x.is_contiguous())  # Output: True

# Create a non-contiguous view with stride 2 in the first dimension
z = x[::2, :]

# Check contiguity of the view
print("z (sliced with stride) is contiguous:", z.is_contiguous())  # Output: False

Explanation:

• In both examples, we start with a contiguous tensor x.
• In the first example, y is created by transposing x. While y shares the underlying data with x, its memory access pattern is non-contiguous due to the swapped dimensions.
• In the second example, z is a slice of x with a stride of 2 in the first dimension. This means it skips elements while accessing data, resulting in a non-contiguous view.

These examples highlight the difference between contiguous and non-contiguous memory layouts and how certain operations can introduce non-contiguity.

Operations That Preserve Contiguity:

• If possible, use operations that inherently create contiguous tensors. For instance, element-wise operations like addition (+) or multiplication (*) between contiguous tensors generally produce new contiguous tensors.

Reshaping with view():

• In some cases, reshaping a non-contiguous tensor with .view() can make it contiguous, provided the new shape is compatible with the underlying data and strides. However, be cautious as .view() might not always guarantee contiguity.

Operations on the Original Tensor:

• If the operation you want to perform works efficiently on non-contiguous tensors, you might not need to convert to a contiguous version right away. For example, some GPU operations might handle non-contiguous memory layout adequately.

Working with Views Directly:

• Sometimes, you can work directly with the non-contiguous view as long as you're aware of its access pattern. This can be memory-efficient, but requires careful handling of indexing and potential performance implications.

Choosing the Right Approach:

The best approach depends on the specific operation you're performing and your performance requirements. Here's a general guideline:

• If the operation is highly sensitive to memory access patterns (especially on GPUs) and the data size is significant, creating a contiguous copy with .contiguous() might be worthwhile.
• If memory optimization is crucial, consider using non-contiguous views directly if the operation supports them.
• If the operation preserves contiguity naturally or the performance impact of non-contiguous memory is negligible, you might not need to create a contiguous copy.

Remember:

• Always check for contiguity using .is_contiguous() when working with non-contiguous tensors to ensure proper handling.
• Profile your code to benchmark the performance difference between using contiguous vs. non-contiguous tensors in your specific use case. This will help you make informed decisions.