Efficiently train models with large sequence lengths using Amazon SageMaker model parallel

Large language models (LLMs) have witnessed an unprecedented surge in popularity, with customers increasingly using publicly available models such as Llama, Stable Diffusion, and Mistral. Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. Although these advancements offer remarkable capabilities, they also present significant challenges. Longer sequence lengths and the sheer number of trainable parameters demand innovative approaches to model development and deployment. To maximize performance and optimize training, organizations frequently need to employ advanced distributed training strategies.

In this post, we demonstrate how the Amazon SageMaker model parallel library (SMP) addresses this need through support for new features such as 8-bit floating point (FP8) mixed-precision training for accelerated training performance and context parallelism for processing large input sequence lengths, expanding the list of its existing features.

We ...