What is Megatron-LM and its tensor parallel approach?

Megatron-LM is a scalable architecture for training large language models across multiple GPUs. The main idea is to efficiently parallelize computations to accelerate training and utilize cluster resources.

Tensor parallelism is an approach where individual tensors (e.g., neural network layer weights) are split across multiple GPUs. Instead of copying the entire model to each GPU, parts of tensors are distributed among devices, and computations are performed in parallel.

This allows:

• Training models that do not fit into the memory of a single GPU.
• Reducing training time through parallel operation.

In Megatron-LM, tensor parallelism is combined with other types of parallelism (e.g., pipeline parallelism) for maximum efficiency.