The Llama family of large language models (LLMs) by Meta AI ranges from 7 billion to 70 billion parameters. Llama uses a transformers-based decoder-only model architecture, specializing in language token generation. Training Llama models from scratch requires trillions of tokens in a dataset. While popular, training Llama models can be technically challenging, time-consuming, and expensive.
To accelerate the full pre-training of Llama models, scaling up to 128 trn1.32xlarge nodes can be used as demonstrated with a Llama 2-7B model example. Best practices for training LLMs on AWS Trainium, scaling on a cluster with over 100 nodes, improving efficiency, recovery from hardware failures, and achieving convergence are shared. The quality of Llama 2-7B trained on Trainium is comparable to the open-source version on various tasks. The benefits of scaling on Trainium are also demonstrated.
Training large-scale LLMs requires distributed training across over 100 nodes, presenting challenges in accessing high-performance compute clusters, maintaining hardware stability, and ensuring model training stability and convergence. The post addresses these challenges and provides solutions for distributed training infrastructure efficiency and scalability, efficient hardware and system recovery, and training stability and convergence.
The setup for Llama 2-7B pre-training involves components like an EC2 cluster, orchestration using Amazon EKS, and container build with custom Docker images. Data preparation includes converting files into a compatible format and optimizing storage and access. Model hyperparameters and training setup are discussed in detail, including distributed training infrastructure efficiency and scalability with optimizations using the Neuron SDK.
The post highlights the hardware and system recovery methods, training stability improvements, and monitoring techniques to ensure model convergence. Model quality evaluation and throughput scalability results are presented, showing the efficiency of scaling Llama models on Trainium accelerators. The importance of model quality improvement through fine-tuning for specific tasks is emphasized.
In conclusion, the post provides an end-to-end training example for the Llama 2-7B model on Trainium accelerators, addressing challenges and sharing best practices for large model training. The authors have extensive experience in AI, ML, HPC, and system optimization, showcasing their expertise in the field. The results demonstrate the effectiveness of training Llama models on Trainium accelerators for high-quality model training.
Article Source
https://aws.amazon.com/blogs/machine-learning/end-to-end-llm-training-on-instance-clusters-with-over-100-nodes-using-aws-trainium/
