Large language model (LLM) training has become increasingly popular in recent times, with the introduction of popular models such as Llama 2, Falcon, and Mistral. Customers are now focused on optimizing LLM performance for various industries, including healthcare, finance, and marketing, by training and fine-tuning models with billions to over 175 billion parameters. However, training models at this scale presents several challenges.
Highly accurate LLMs require massive amounts of training data and significant compute resources, such as thousands or even millions of hours of accelerator compute time, to achieve the desired accuracy. To address these challenges, customers rely on parallelism techniques to distribute the workload across thousands of accelerator devices. However, utilizing these techniques can be challenging due to compatibility issues, sensitivity to configurations, and the rapidly evolving state of the art.
To simplify and expedite the large model training process, Amazon SageMaker has introduced new features in its model parallel (SMP) library. These features enhance the user experience, expand tensor parallel functionality, and optimize performance, resulting in up to 20% reduction in training time and cost. The SMP library aligns its APIs with open source PyTorch, making it easier to integrate with existing PyTorch Fully Sharded Data Parallel (FSDP) training scripts.
Furthermore, SMP v2.0 introduces tensor parallelism, which allows training on massive clusters without affecting model convergence. By combining sharded data parallelism with tensor parallelism, customers can increase training throughput by provisioning clusters with 256 nodes or more. SMP v2.0 seamlessly integrates with the Transformer Engine and PyTorch FSDP APIs, enabling stable training on large clusters without requiring changes to the PyTorch model or FSDP configuration.
In addition to these advancements, SMP offers optimization techniques that can accelerate model training by up to 20%. One such technique is hybrid sharding, which optimizes memory consumption and communication overhead. By specifying the degree of sharding in the SMP configuration, customers can find the optimal balance for their training workload. The SMP library also leverages optimized collective communication operations, such as AllGather, provided by the SageMaker distributed data parallelism (SMDDP) library to further enhance performance on AWS infrastructure.
Overall, these new features in the SMP library simplify and accelerate large model training. Customers can leverage the power of SageMaker and the SMP library without major modifications to their existing PyTorch FSDP training scripts. By incorporating parallelism techniques and optimization strategies, customers can train LLMs more efficiently and launch products in a timely manner.
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