Safeguarding Gradients in PyTorch: When to Use .detach() Over .data

In PyTorch versions before 0.4.0:

• Tensors were represented by Variable objects, which tracked computation history for automatic differentiation (autograd).
• To access the underlying tensor data without the computation history, you used the .data attribute:

x = torch.tensor([1, 2, 3], requires_grad=True)  # Create a tensor with gradient tracking
y = x.data  # Get a new tensor sharing the same data as x, but without requires_grad=True

From PyTorch 0.4.0 onwards:

• While .data still exists for backward compatibility, it's generally discouraged due to potential issues:

  • Modifications made to x.data wouldn't be tracked by autograd, leading to incorrect gradients if x is used in backpropagation.

y = x.detach()  # Preferred way to get a new tensor without requires_grad=True

• .detach() offers the same benefits as .data (creates a new tensor with shared data and requires_grad=False), but it ensures that in-place modifications to y are reported by autograd if x is needed in the backward pass.

Key Points:

• Use .detach() for clarity and safety in modern PyTorch versions.
• Be aware of potential issues with .data if you're working with older code.

Additional Considerations:

• If you absolutely need to modify the underlying data without affecting the computation graph (rare cases), you can use x.data.copy(). However, exercise caution and understand the implications for autograd.

By understanding these concepts, you can effectively work with tensor data in PyTorch while maintaining correct gradient calculations for your deep learning models.

Using .data (Pre-0.4.0 behavior):

import torch

# Create a tensor with gradient tracking
x = torch.tensor([1, 2, 3], requires_grad=True)

# Access the underlying data (not recommended in newer versions)
y = x.data

# Modify y (won't affect gradients of x)
y += 1

# This will print [1, 2, 3], as the change to y doesn't affect x
print(x)

Using .detach() (Recommended):

import torch

# Create a tensor with gradient tracking
x = torch.tensor([1, 2, 3], requires_grad=True)

# Create a new tensor without requires_grad (preferred method)
y = x.detach()

# Modify y (won't affect gradients of x)
y += 1

# This will print [1, 2, 3], as the change to y doesn't affect x
print(x)

# Now, let's perform some operations using x and backpropagate
z = x * 2
loss = z.sum()
loss.backward()

# This will correctly calculate gradients for x since y was detached
print(x.grad)

As you can see, .detach() is the preferred approach in modern PyTorch versions because it ensures that modifications to the detached tensor don't interfere with the computation graph and autograd calculations.

1. Creating a New Tensor from Scratch:

   This is the most straightforward approach if you don't need to share the underlying data with the original tensor:

   import torch
   
   x = torch.tensor([1, 2, 3], requires_grad=True)
   y = torch.tensor(x.numpy())  # Convert x to NumPy array and create a new tensor from it
   

   Here, y is a completely new tensor with the same values as x, but without the computation history (requires_grad=False).

2. clone() (with Caution):

   The .clone() method creates a new tensor that shares the same underlying data with the original tensor, but has its own independent computation history. However, use this with caution for the following reasons:

   • Modifications to y might still affect x in backpropagation if certain operations are performed on them. This is because they share the underlying data.
   • Disabling gradient tracking with .requires_grad=False on y doesn't entirely prevent potential issues.

   Here's an example (use with care):

   import torch
   
   x = torch.tensor([1, 2, 3], requires_grad=True)
   y = x.clone().detach()  # Clone and then detach to ensure independent computation history
   

   In this case, y shares the data with x but has requires_grad=False. However, if you modify y in certain ways, it might still affect the gradients of x. It's generally safer to avoid .clone() for this purpose.

Remember that the preferred approach for most cases is using .detach(). It provides a clear and safe way to create a new tensor without the computation history, ensuring correct behavior in your deep learning models.