Disabling Gradient Tracking in PyTorch: torch.autograd.set_grad_enabled(False) vs. with no_grad()

PyTorch's automatic differentiation (autograd) engine is a powerful tool for training deep learning models. It efficiently calculates gradients, which are essential for optimizing model parameters during training. By default, gradients are tracked for tensors that have requires_grad=True.

Disabling Gradient Tracking

There are scenarios where you might want to disable gradient tracking for performance reasons or to prevent unnecessary computations. Here's how set_grad_enabled(False) and with no_grad() achieve this:

• torch.autograd.set_grad_enabled(False):

  • This function globally sets the gradient tracking mode for the entire PyTorch runtime.
  • When set to False, gradients are not computed for any tensors, regardless of their requires_grad setting.
  • Use this with caution as it affects all operations within the scope where it's set.

• with torch.no_grad()::**

  • This context manager creates a temporary scope where gradient tracking is disabled.
  • Any operations performed within this block will not calculate gradients, even for tensors with requires_grad=True.
  • Once you exit the with block, the previous gradient tracking mode is restored.
  • This is generally preferred for localized disabling of gradients.

Choosing the Right Approach

• Global Disabling (set_grad_enabled(False)):

  • Use this sparingly, for specific use cases where you need to completely turn off gradient tracking across your entire program (e.g., evaluating a pre-trained model on a fixed dataset).
  • Be mindful of potential side effects on other parts of your code that might rely on gradients.

• Localized Disabling (with no_grad()):

  • This is the recommended approach for most cases where you want to disable gradients for a specific block of code (e.g., forward pass during inference, preprocessing steps that don't affect the model).
  • It's more granular and avoids unintended consequences on other parts of your code.

Example:

import torch

# Model (assume parameters have requires_grad=True)
model = ...

# Global disabling (use with caution)
torch.autograd.set_grad_enabled(False)
outputs = model(inputs)  # No gradients computed

# Localized disabling (recommended)
with torch.no_grad():
    outputs = model(inputs)  # No gradients computed in this block only

# Back to normal gradient tracking
torch.autograd.set_grad_enabled(True)  # Restore previous mode (if globally disabled)

import torch

# Sample model (assume parameters have requires_grad=True)
class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        # ... (define your model layers here)

    def forward(self, x):
        # ... (define your model's forward pass)
        return output

model = MyModel()

# Input data
inputs = torch.randn(1, 5)  # Example input

# Forward pass with gradient tracking disabled (inference)
with torch.no_grad():
    outputs = model(inputs)

print("Outputs (no gradients computed):", outputs)

# Gradient tracking is automatically re-enabled after exiting the `with` block

In this example:

• The with torch.no_grad(): context manager temporarily disables gradient tracking for the operations within the block.
• The model(inputs) call performs the forward pass without calculating gradients for the model's parameters.
• After exiting the with block, gradient tracking resumes its previous state.

Example 2: Global Disabling (Use with Caution) with set_grad_enabled(False)

import torch

# ... (same model definition as Example 1)

# Disable gradient tracking globally (use cautiously)
torch.autograd.set_grad_enabled(False)

# Forward pass (no gradients)
outputs = model(inputs)

print("Outputs (globally disabled gradients):", outputs)

# Re-enable gradient tracking (if needed)
torch.autograd.set_grad_enabled(True)

• torch.autograd.set_grad_enabled(False) turns off gradient tracking for all operations throughout the script's execution.
• The model(inputs) call doesn't compute gradients for the model's parameters.
• Remember to re-enable gradient tracking using torch.autograd.set_grad_enabled(True) if necessary for other parts of your code that rely on gradients.

Key Points:

• Use with torch.no_grad() for localized disabling within specific code blocks (generally preferred approach).
• Use torch.autograd.set_grad_enabled(False) globally with caution, considering its impact on the entire script.
• Choose the method that best suits your specific situation.

1. requires_grad Attribute:

   • You can directly control whether a specific tensor tracks gradients by setting its requires_grad attribute to False. This can be useful for tensors that you don't intend to use for backpropagation (e.g., intermediate calculations, constants).
   • However, this approach can become cumbersome if you need to disable gradients for many tensors within a block of code. That's where with no_grad() shines.

2. torch.detach():

   • The torch.detach() function creates a new tensor that is a detached copy of the original tensor. The detached copy does not track gradients, even if the original tensor did.
   • This can be useful for isolating specific outputs from a computation and preventing them from influencing gradients.
   • However, torch.detach() creates a new tensor, which can have memory implications for large tensors.

• For localized disabling within a block of code: Use with no_grad() - it's concise and efficient.
• For individual tensors you don't need gradients for: Set the requires_grad attribute to False.
• For isolating specific outputs without affecting the original tensor: Use torch.detach(), but be mindful of memory usage for large tensors.

Example: Using requires_grad Attribute

import torch

x = torch.randn(2, 3, requires_grad=True)  # Track gradients for x
y = torch.randn(2, 3)  # No gradients for y (default)

# Intermediate calculation without gradients
z = x * y  # z won't track gradients because y doesn't

# Further calculations with gradients for x
result = x + z

# Backpropagation will only consider gradients for x