In-place vs. Out-of-place Addition in PyTorch: torch.Tensor.add_ vs. Alternatives

• torch.Tensor.add_ is an in-place operation that performs element-wise addition on a PyTorch tensor.
• It modifies the original tensor (self) by adding the corresponding elements of another tensor or a constant value.
• Unlike torch.add, which creates a new tensor, add_ directly alters the existing tensor, potentially saving memory.

Syntax:

tensor.add_(other)

• tensor: The PyTorch tensor to be modified in-place.
• other: The tensor or constant value to be added element-wise.

Example:

import torch

a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])

# In-place addition using add_
a.add_(b)
print(a)  # Output: tensor([5, 7, 9])

# Regular addition using add (creates a new tensor)
c = torch.add(a, b)
print(c)  # Output: tensor([5, 7, 9]) (different object than a)

Key Points:

• In-place Operation: add_ modifies the original tensor, making it suitable when memory usage is a concern. However, it can be less intuitive for debugging and might affect gradient calculations (discussed later).
• Element-wise Addition: The addition is performed on corresponding elements between the tensors or between the tensor and the constant value.
• Data Type Compatibility: The data types of the tensors involved must be compatible for addition.

When to Use add_:

• When memory efficiency is critical, and you don't need to preserve the original tensor.
• When dealing with very large tensors, creating new tensors might be expensive.

Cautions:

• Debugging: In-place operations can make debugging trickier as the original tensor is modified directly. Consider using torch.add if you need to preserve the original values for debugging purposes.
• Gradient Calculation: When using add_ with tensors that require gradient calculation (tensors with requires_grad=True), it might affect how gradients are computed. In such cases, torch.add is generally recommended.

import torch

# Create a tensor
a = torch.tensor([1, 2, 3])

# Add 5 to each element of a in-place
a.add_(5)

# Print the modified tensor
print(a)  # Output: tensor([6, 7, 8])

Element-wise Addition of Tensors In-place:

import torch

# Create two tensors
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])

# Add elements of b to corresponding elements of a in-place
a.add_(b)

# Print the modified tensor a
print(a)  # Output: tensor([5, 7, 9])

Using add_ within a Loop (Caution with Gradient Calculation):

import torch

# Create a tensor requiring gradient
x = torch.tensor([1.0, 2.0], requires_grad=True)

# In-place addition within a loop (might affect gradients)
for _ in range(3):
  x.add_(1.0)  # Add 1 to each element in-place

# Print the modified tensor
print(x)  # Output: tensor([4., 5.], requires_grad=True)

# Calculate gradients (might be affected by in-place operations)
x.backward()  # This might not produce the expected gradients due to in-place operations

# Consider using torch.add if gradient calculation is crucial

Remember:

• Use add_ when memory efficiency is a concern, but be cautious with debugging and gradient calculations.
• For debugging or when gradients are important, use torch.add to create a new tensor without modifying the original one.

• This is the most common and straightforward alternative. It creates a new tensor containing the element-wise sum of the input tensors or tensor and constant value.
• Syntax: result = torch.add(tensor1, tensor2) (or result = torch.add(tensor, constant))
• Advantage: Preserves the original tensors, making it safer for debugging and ensuring gradients are calculated correctly.
• Disadvantage: Creates a new tensor, which might use more memory compared to add_.

Arithmetic Operator (+) with Element-wise Broadcasting:

• PyTorch supports element-wise addition using the + operator along with broadcasting.
• Broadcasting allows tensors of different shapes to be added as long as their compatible dimensions have the same size.
• Syntax: result = tensor1 + tensor2 (or result = tensor + constant)
• Advantage: Concise syntax, familiar to those with experience in other programming languages.
• Disadvantage: Similar memory usage considerations as torch.add. Might be less readable for complex operations.

List Comprehension (for Tensors with Compatible Shapes):

• While not the most efficient approach for large tensors, you can use list comprehension to create a new tensor with the element-wise sum.
• Syntax: result = [a + b for a, b in zip(tensor1, tensor2)]
• Advantage: Can be used for simple in-place-like behavior when memory usage isn't critical (modifies a list instead of a tensor).
• Disadvantage: Less efficient for large tensors compared to other methods, might not be suitable for complex operations.

Choosing the Right Method:

• Memory Efficiency: If memory is a major concern, and you don't need to preserve the original tensors or gradients, torch.Tensor.add_ can be a good choice.
• Debugging and Gradient Calculation: For debugging purposes or when working with tensors that require gradient calculation (requires_grad=True), use torch.add or the + operator to ensure the original tensors and gradients are not affected.
• Readability and Simplicity: For simple element-wise addition, the + operator with broadcasting offers a concise and familiar syntax.

• torch.add and the + operator are generally safer choices for most cases, especially when debugging or gradients are involved.
• Use torch.Tensor.add_ cautiously, considering its potential impact on debugging and gradient calculations.
• The best method depends on your specific needs regarding memory usage, debugging, gradient calculation, and code readability.