What is the most popular parallelization technique for training extremely large LLMs with billions of parameters? (A) Data parallelism (B) Model parallelism (C) Task parallelism
Answer (1)
The question is about the most popular parallelization technique for training extremely large language models (LLMs) with billions of parameters. In the context of training such large models, there are a few key parallelization techniques that are commonly used:
Data Parallelism (A):
In data parallelism, the same model is replicated across multiple GPUs or nodes, and each replica is fed a different subset of the training data. The computations for each subset can proceed in parallel, and gradients are averaged across replicas to update the model parameters. This method is often used when the model itself can fit into the memory of a single GPU or node.
Model Parallelism (B):
Model parallelism involves splitting a single model across multiple GPUs or nodes. Each GPU handles a portion of the model, allowing the training of models that are too large to fit into the memory of a single GPU. This is especially useful for very large models, like those with billions of parameters, where even a single layer might exceed the memory limits of individual hardware units.
Task Parallelism (C):
Task parallelism involves dividing tasks into smaller subtasks that can be processed simultaneously but does not directly apply to training a single neural network model. It is more common in situations where different tasks or operations need to be handled concurrently.
For training extremely large LLMs with billions of parameters, Model Parallelism (B) is typically the most suitable and popular technique due to the sheer size of these models. Model parallelism allows for distributing the architecture across several devices to leverage the combined memory and computational power, making it feasible to train such large models efficiently.
