Understanding PyTorch Tensor Assignment Behavior: A Subtle Gotcha

PyTorch is one of the most popular deep learning frameworks, known for its intuitive design and flexible tensor operations. However, like any complex system, it has some subtle behaviors that can catch even experienced developers off guard. In this blog post, we'll explore an interesting behavior related to tensor assignment and swapping operations.

The Puzzling Behavior

Let's look at a simple example that demonstrates an unexpected behavior:

import torch

# Create two random tensors
a = torch.rand((2, 2))
b = torch.rand((2, 2))

print("Original a:\n", a)
print("Original b:\n", b)

# Swap the tensors
a, b = b, a

print("After a, b = b, a:")
print("a =\n", a)
print("b =\n", b)

# Attempt to swap the first rows
a[0], b[0] = b[0], a[0]

print("After a[0], b[0] = b[0], a[0]:")
print("a =\n", a)
print("b =\n", b)

When we run this code, we get an output similar to the following:

Original a:
 tensor([[0.6606, 0.7158],
        [0.9971, 0.1011]])
Original b:
 tensor([[0.2048, 0.3493],
        [0.6233, 0.9587]])
After a, b = b, a:
 a =
 tensor([[0.2048, 0.3493],
        [0.6233, 0.9587]])
b =
 tensor([[0.6606, 0.7158],
        [0.9971, 0.1011]])
After a[0], b[0] = b[0], a[0]:
 a =
 tensor([[0.6606, 0.7158],
        [0.6233, 0.9587]])
b =
 tensor([[0.6606, 0.7158],
        [0.9971, 0.1011]])

Wait, what just happened? The first swap operation a, b = b, a worked perfectly as expected, but the second one a[0], b[0] = b[0], a[0] didn't behave as we might have anticipated. Instead of swapping the first rows, both a[0] and b[0] ended up with the same values!

Understanding the Behavior

To understand what's happening, we need to dive into how PyTorch handles tensor indexing and assignment operations.

Variable Swapping in Python

When we write a, b = b, a in Python, the right side of the assignment is evaluated first, creating temporary copies of the values, and then these values are assigned to the variables on the left side. This is why the complete tensor swap works as expected.

Tensor Indexing and Views

The key to understanding the unexpected behavior lies in how PyTorch handles tensor indexing. When you index a tensor like a[0], you're not creating a new tensor but rather a view into the original tensor. A view is essentially a new tensor object that shares the same underlying data with the original tensor.

When we attempt a[0], b[0] = b[0], a[0], the following happens:

The right side is evaluated first: b[0], a[0] creates views into the current state of b and a.
When we assign b[0] (which is a view of b's first row) to a[0], we're modifying the first row of a.
Now, the crucial part: the original value of a[0] has already been changed when we try to assign it to b[0]