Day 24: Gradient Accumulation

Introduction

In the world of deep learning, training large models with limited GPU memory can be challenging. One common technique to address this issue is Gradient Accumulation. This method allows training with larger effective batch sizes without exceeding GPU memory limits.

What is Gradient Accumulation?

Gradient accumulation is a process where gradients are calculated over multiple forward passes before updating the model weights. Instead of updating weights after each batch, the optimizer updates them after a defined number of batches (or steps) called an accumulation step.

Key Concept

• Effective Batch Size: It is the product of the actual batch size per GPU and the number of accumulation steps.
• If you can only fit batch_size=16 on your GPU but want an effective batch size of 64, you can accumulate gradients over 4 steps (accumulation_steps=4).

How Does It Work?

1. Forward pass is performed, and gradients are calculated for batch_1.
2. Gradients are not immediately used to update the weights; instead, they are accumulated.
3. Forward pass and gradient calculation are repeated for batch_2, batch_3, and so forth.
4. After the specified accumulation_steps, the accumulated gradients are used to update the weights.
5. Gradients are reset, and the process repeats.

Benefits of Gradient Accumulation

• Memory Efficiency: Enables training with a larger effective batch size without exceeding memory limits.
• Stability: Larger batch sizes often lead to more stable training and better generalization.

Example Code (PyTorch)

# Assume model, optimizer, and loss function are defined
accumulation_steps = 4  # Number of steps to accumulate gradients

for i, (inputs, labels) in enumerate(data_loader):
    outputs = model(inputs)
    loss = loss_function(outputs, labels)
    loss = loss / accumulation_steps  # Normalize loss for accumulation
    loss.backward()  # Accumulate gradients

    if (i + 1) % accumulation_steps == 0:
        optimizer.step()  # Update weights
        optimizer.zero_grad()  # Reset gradients

When to Use Gradient Accumulation?

• When you want to use a batch size larger than what fits in your GPU memory.
• When training deep or complex models with limited resources.

Conclusion

Gradient accumulation is a simple yet powerful technique for overcoming hardware limitations and ensuring efficient training of large-scale models. It allows researchers and engineers to push the boundaries of model training without needing access to cutting-edge hardware.