Generative AI with LLMs: Data Parallelism and Model Parallelism: How to Combine Them to Train LLMs

Learn what data parallelism and model parallelism are and how they can be used to distribute the training workload of large language models (LLMs) across multiple devices. Discover how to combine data parallelism and model parallelism to train LLMs that are too large or complex to fit into a single device or a group of devices.

Question

“You can combine data parallelism with model parallelism to train LLMs.” Is this true or false?

A. True
B. False

Answer

A. True

Explanation

The correct answer is A. True. You can combine data parallelism with model parallelism to train LLMs. Data parallelism and model parallelism are two paradigms of parallelism that can be used to distribute the training workload of large language models (LLMs) across multiple devices, such as GPUs or TPUs. Data parallelism and model parallelism can be used independently or together, depending on the size and complexity of the model and the data.

Data parallelism is when you use the same model for every device, but feed it with different parts of the data. For example, if you have four devices and a dataset of 1000 samples, you can split the dataset into four parts of 250 samples each, and assign each part to a different device. Each device will then compute the forward and backward passes of the model using its own data, and then exchange the gradients with the other devices. The gradients are then averaged and used to update the model parameters. Data parallelism can speed up the training process by processing more data in parallel, but it also requires more communication and synchronization between the devices.

Model parallelism is when you use the same data for every device, but split the model among devices. For example, if you have four devices and a model with four layers, you can assign each layer to a different device. Each device will then compute the forward and backward passes of its own layer using the same data, and then pass the intermediate outputs and gradients to the next or previous device. Model parallelism can reduce the memory usage and communication cost by dividing the model into smaller parts, but it also introduces more dependencies and latency between the devices.

Data parallelism and model parallelism can be combined to train LLMs that are too large or complex to fit into a single device or a group of devices. For example, if you have 16 devices and a model with eight layers, you can use data parallelism to split the data into four parts, and use model parallelism to split the model into four parts. Each part of the model will then be replicated on four devices, and each device will process a different part of the data. This way, you can leverage both the advantages of data parallelism and model parallelism, and achieve higher scalability and efficiency.

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