Alternatives to torch.Tensor.sign_: Efficient Sign Extraction in PyTorch

There appears to be a slight misunderstanding in the query.

In PyTorch, there isn't a method directly called torch.Tensor.sign_. However, there are two closely related concepts:

1. torch.sign Function
   This is a function that operates on a PyTorch tensor and returns a new tensor containing the signs of the elements in the input tensor.

   • Behavior
     

     • For positive values, it returns 1.
     • For negative values, it returns -1.
     • For zero, it returns 0.

   • Example
     

     import torch
     
     a = torch.tensor([0.7, -1.2, 0., 2.3])
     print(torch.sign(a))  # Output: tensor([ 1., -1.,  0.,  1.])
     

Key Points

• In-place operations modify the original tensor, so use them with caution.
• The in-place version (torch.Tensor.sign_) might exist in custom code, but using torch.sign is preferred.
• Use torch.sign to get the signs of elements in a PyTorch tensor.

---

Using torch.sign function

import torch

# Create a sample tensor
a = torch.tensor([3, -2, 0, 5.1])

# Get the signs of elements
signs = torch.sign(a)
print(signs)  # Output: tensor([ 1., -2.,  0.,  1.])

# Example usage with conditional logic (avoiding in-place for clarity)
if torch.all(signs == 1):
    print("All elements are positive or zero.")
else:
    print("There are non-positive elements.")

Creating a Custom In-Place sign_ Function (Not Recommended for General Use)

import torch

def sign_(tensor):
    """
    In-place sign function (not recommended for general use).
    """
    tensor.where(tensor > 0, torch.tensor(1), torch.where(tensor < 0, torch.tensor(-1), torch.tensor(0)))

# Create a tensor
a = torch.tensor([3, -2, 0, 5.1])

# Modify the tensor in-place (use with caution)
sign_(a)
print(a)  # Output: tensor([ 1., -2.,  0.,  1.])

• It uses torch.where to conditionally assign values based on the sign:
  • If tensor is greater than 0, it assigns 1.
  • If tensor is less than 0, it assigns -1.
  • Otherwise, it assigns 0.
• This function iterates through the elements of the input tensor.

• While this code demonstrates an in-place function, it's generally recommended to use the functional version (torch.sign) for clarity and to avoid modifying the original tensor unintentionally. In-place operations can lead to unexpected side effects in complex code.

---

1. torch.sign function
   This is the recommended and most straightforward approach. It creates a new tensor containing the signs of the elements in the input tensor. This is a functional operation, meaning it doesn't modify the original tensor.

   import torch
   
   a = torch.tensor([3, -2, 0, 5.1])
   signs = torch.sign(a)  # signs will be a new tensor with signs of elements in a
   print(signs)  # Output: tensor([ 1., -2.,  0.,  1.])
   

2. Conditional expression with torch.where
   This allows you to create a new tensor with the desired signs using a more explicit approach.

   import torch
   
   a = torch.tensor([3, -2, 0, 5.1])
   signs = torch.where(a > 0, torch.tensor(1), torch.where(a < 0, torch.tensor(-1), torch.tensor(0)))
   print(signs)  # Output: tensor([ 1., -2.,  0.,  1.])
   

Why avoid in-place operations (like a hypothetical sign_)

• Debugging
  It's easier to debug code when you can track the changes to tensors explicitly through functional operations.
• Immutability
  Functional operations create new tensors, which helps maintain the immutability of the original data and avoids potential side effects.
• Clarity
  Using the functional version (torch.sign) makes the code easier to understand and reason about.