Batch

Batch Processing and Micro-batching are integral concepts in the training of large language models. These processes efficiently adjust model parameters to minimize prediction errors.

Batching

In Machine Learning, a batch refers to a subset of the total training data. Instead of processing the entire dataset at once, the data is split into smaller chunks or batches. This approach optimizes memory usage and allows the model to begin learning before loading the entire dataset.

Batch sizes can range from one example (a process known as stochastic gradient descent) to many thousands. A trade-off exists: smaller batches can converge faster and require less memory but are more susceptible to noise in gradient estimates. Larger batches give a more accurate gradient estimate but are computationally demanding and memory-intensive.

Micro-batching

Micro-batching is a technique used when a model or batch size is too large to fit into memory. Each batch is divided into smaller subsets known as micro-batches. The model processes one micro-batch at a time, computes the gradient, but does not immediately apply the gradient update. Instead, the gradients of several micro-batches are accumulated, and the average gradient is then used to update the model parameters. This approach maintains computational efficiency while allowing for larger batch sizes and more stable gradient estimates.

Impact of Batch Size on Learning

The choice of batch size during the training of a language model (or any machine learning model) significantly influences both the learning speed and the quality of the learned parameters.

Learning Speed

Batch size directly impacts the computational efficiency of the training process:

• Larger batch sizes allow for better utilization of hardware resources, especially when using GPUs. By processing multiple examples simultaneously, the computation can be parallelized, which generally leads to faster training times per epoch. However, this requires more memory, which might be a limiting factor depending on the available hardware.
• Smaller batch sizes require less memory and thus can be run on machines with less powerful hardware. However, they might not fully utilize the computational resources and therefore might result in slower training times per epoch.

Quality of Learned Parameters

Batch size also impacts the model's ability to generalize:

• Larger batch sizes provide a more accurate estimate of the gradient. Theoretically, this should lead to more stable and reliable updates to the parameters, and therefore a smoother convergence. However, it has been observed that training with larger batch sizes often results in models that generalize less well. One explanation for this is that larger batches tend to converge to shallow minima in the loss landscape, which correspond to worse generalization performance.
• Smaller batch sizes provide a noisier estimate of the gradient. This noise can have a beneficial regularizing effect, in a way similar to adding explicit regularization terms (like weight decay) to the loss function. Consequently, models trained with smaller batches often generalize better. On the downside, the noisiness of the gradient updates can also lead to less stable training and a harder-to-control convergence.

The optimal batch size can vary depending on the specific task, the architecture of the model, the size and nature of the dataset, and the computational resources available. Often, it is determined through experimentation and hyperparameter tuning.

See Also

• Backpropagation
• Stochastic Gradient Descent
• Learning Rate
• Loss Function