Gradient Accumulation

What is Gradient Accumulation? 

Imagine you have to fine-tune a LLM, but you only have a small number of GPUs, making your training memory-constrained. Or imagine you want to train an image classifier but you don't have enough GPU memory. In these cases, Gradient Accumulation can help. Gradient Accumulation is a technique used when training neural networks to support larger batch sizes given limited available GPU memory.

In traditional (mini-batch) stochastic gradient descent (SGD), training is performed in batches, primarily to improve throughput (reduce training time).  During the forward pass, a batch is fed into the model, and gradients with respect to the loss function are computed. The model's parameters are then updated after computing the gradients on a single batch of training data.

However, in Gradient Accumulation, instead of updating the model parameters after processing each individual batch of training data, the gradients are accumulated over multiple batches before updating. This means that rather than immediately incorporating the information from a single batch into the model's parameters, the gradients are summed up over multiple batches. Once a certain number of batches have been processed (typically denoted as N batches), the accumulated gradients are used to update the model parameters. This update can be performed using any optimization algorithm like SGD or Adam. This approach reduces the amount of memory needed for training and can help stabilize the training process, particularly when working with the batch size is too large to fit into the memory.

What are the Advantages of Gradient Accumulation?

The main advantages of gradient accumulation are:

• Memory Efficiency: It allows training with larger effective batch sizes without requiring additional memory. This can be crucial when working with limited computational resources or when dealing with large models.
• Stable Training: Accumulating gradients over multiple batches can provide a more stable update direction, especially when dealing with noisy gradients. Accumulating gradients over multiple batches can reduce the impact of noisy gradients, which may arise due to the inherent randomness in sampling mini batches. By accumulating gradients, the batch size seen by the optimizer can be effectively increased, which can lead to more stable updates and better utilization of hardware resources.
• Improved Generalization: Some studies suggest that Gradient Accumulation can lead to better generalization performance by effectively increasing the effective batch size during training.

Implementing Gradient Accumulation 

When introducing Gradient Accumulation for training machine learning models, it's essential to understand the various considerations that come into play to ensure its effective use.

• Learning Rate Adjustment: Adjusting the learning rate is often necessary when using Gradient Accumulation. Since the effective batch size increases with accumulation, the learning rate might need to be scaled accordingly to ensure stable training. A common approach is to divide the learning rate by the accumulation factor, which is the number of batches accumulated before updating the parameters. This adjustment helps maintain a consistent learning rate relative to the effective batch size.
• Convergence Behavior: Gradient Accumulation can impact the convergence behavior of the training process. Depending on factors such as the accumulation factor and the learning rate adjustment strategy, the training dynamics may change. It's essential to monitor the training process and experiment with different accumulation strategies to ensure convergence to an optimal solution. In some cases, excessive accumulation might lead to slower convergence or even hinder convergence altogether, so finding the right balance is crucial.
• Computational Overhead: While Gradient Accumulation can be memory-efficient, it may introduce additional computational overhead. Accumulating gradients over multiple batches requires storing the gradients for each parameter until they are updated, which can increase memory usage and computation time. It's essential to consider the trade-offs between memory efficiency and computational overhead when deciding on the accumulation strategy, especially when working with limited resources.

Gradient Accumulation in ML Frameworks

Axolotl supports gradient accumulation for open-source models like Llama-2 and Mistral, by adding to the Axolotl yaml config file:

gradient_accumulation_steps: N

Axolotl can be used for fine-tuning models on Hopsworks by simply installing it as a Python dependency in your project. Your fine-tuning training data can be loaded from Hopsworks by Axolotl using the built-in FUSE support that makes your training data, stored on HopsFS-S3, available as local files to Axolotl.

In summary, Gradient Accumulation is a technique used to improve memory efficiency and stabilize training in neural networks by accumulating gradients over multiple batches before updating the model parameters. Gradient Accumulation offers advantages in terms of memory usage, stability, and potentially improved generalization performance, but it requires careful consideration of implementation details and tuning for optimal results.