Why Gradients Need Resetting During Deep Learning Training with PyTorch

Purpose

• torch.optim.SGD.zero_grad() (or optimizer.zero_grad() for any optimizer class) serves a crucial role in this process by resetting the gradients of all optimized parameters to zero after each backward pass.
• In PyTorch deep learning, gradients are used to update the weights and biases of your model during training. These gradients represent how much each parameter should be adjusted to minimize the loss function.

Why Reset Gradients?

• This becomes especially important when processing data in batches:
  • You calculate gradients for each batch, but updating parameters based on accumulated gradients across all batches would be incorrect.
• Gradients accumulate over multiple training steps. If you don't reset them, they'll keep adding up, leading to inaccurate parameter updates. Imagine climbing a hill (loss function) - you need to start from the bottom (zero gradients) each step to find the steepest descent (optimal weights).

How it Works

1. Backward Pass
   During the backward pass, the loss function is propagated backward through the model, calculating the gradients for each parameter.
2. zero_grad()
   After the backward pass, you call optimizer.zero_grad() to explicitly set the gradients of all parameters tracked by the optimizer to zero. This ensures a clean slate for the next batch or training iteration.

import torch

# Create a model and optimizer
model = ...
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # SGD optimizer with learning rate 0.01

# Training loop
for epoch in range(num_epochs):
    for data, target in train_loader:
        optimizer.zero_grad()  # Reset gradients before each batch
        output = model(data)
        loss = loss_fn(output, target)
        loss.backward()  # Backward pass to calculate gradients
        optimizer.step()  # Update parameters based on current gradients

---

Multiple Optimizers

This example demonstrates using zero_grad() with separate optimizers for different parameter groups:

import torch

# Create model with separate parameter groups (e.g., encoder and decoder)
model = ...

# Define optimizers for each group
encoder_optimizer = torch.optim.SGD(model.encoder.parameters(), lr=0.01)
decoder_optimizer = torch.optim.SGD(model.decoder.parameters(), lr=0.001)

# Training loop
for epoch in range(num_epochs):
    for data in train_loader:
        encoder_optimizer.zero_grad()
        decoder_optimizer.zero_grad()
        # ... rest of training logic ...
        encoder_optimizer.step()
        decoder_optimizer.step()

Custom zero_grad Function (Optional)

While not always necessary, you can create a custom function to call zero_grad() on multiple optimizers:

def reset_grads(optimizers):
  for optimizer in optimizers:
    optimizer.zero_grad()

# Usage
reset_grads([encoder_optimizer, decoder_optimizer])

set_to_none Argument (PyTorch 1.7+)

For newer PyTorch versions, zero_grad() offers an optional set_to_none=True argument. This sets gradients to None instead of zero, potentially reducing memory usage but with slightly different behavior (refer to PyTorch documentation for details).

---

1. Manual Gradient Zeroing
   

• While less convenient, you can manually zero the gradients of each parameter you want to optimize after the backward pass. This involves accessing the .grad attribute of each parameter and setting it to zero using .zero_():

for param in model.parameters():
  if param.requires_grad:
    param.grad.zero_()

1. torch.autograd.grad.zero_grad() (Deprecated)
   

• In older PyTorch versions (pre-1.0), you might encounter torch.autograd.grad.zero_grad(). While functionally similar to optimizer.zero_grad(), it's generally recommended to use the optimizer-specific method for better maintainability and compatibility with newer versions.

Important Considerations

• torch.autograd.grad.zero_grad() is deprecated, so using optimizer.zero_grad() is the preferred approach.
• Manual zeroing can be tedious and error-prone for complex models with many parameters. It's also less efficient as it requires iterating through parameters.

• Custom Optimizers
  For very specific optimization needs, you might consider creating a custom optimizer class that incorporates gradient handling differently. However, this is an advanced approach and typically not necessary for most deep learning tasks.
• Gradient Accumulation
  If memory is a concern during training, you can accumulate gradients over multiple batches before calling optimizer.zero_grad(). This reduces the number of backward passes but requires adjusting the learning rate accordingly.