Mixture of Experts (MoE) architectures for large language models (LLMs) have become increasingly popular due to their ability to enhance model capacity and computational efficiency compared to fully dense models. By utilizing sparse expert subnetworks that process different subsets of tokens, MoE models can effectively increase the number of parameters while requiring less computation per token during training and inference. This allows for more cost-effective training of larger models within fixed compute budgets compared to dense architectures.
Despite their computational advantages, efficiently training and fine-tuning large MoE models presents some challenges. MoE models may struggle with load balancing if tokens are not evenly distributed across experts during training, leading to some experts being overloaded while others are underutilized. Additionally, MoE models have high memory requirements as all expert parameters need to be loaded into memory even though only a subset is used for each input.
To address these challenges, Amazon SageMaker has introduced new features in the model parallelism library that enable efficient training of MoE models using expert parallelism. Expert parallelism involves splitting experts of an MoE model across separate workers or devices, similar to how dense model layers can be partitioned with tensor parallelism.
The Mixtral 8x7B model, for example, has a sparse MoE architecture with eight expert subnetworks containing around 7 billion parameters each. A trainable gate network called a router determines which input tokens are sent to which expert, allowing the experts to specialize in processing different aspects of the input data. By distributing the workload across multiple devices using expert parallelism, MoE training can be more memory-efficient and faster.
In addition to expert parallelism, the SMP library also supports sharded data parallelism, which further reduces the memory footprint of the model by partitioning and distributing experts and non-MoE layers across a cluster. By combining expert parallelism and sharded data parallelism, MoE models can be trained more effectively on larger clusters while maintaining performance.
Overall, leveraging expert parallelism and sharded data parallelism with tools like SMP and SMDDP can significantly improve the efficiency and performance of distributed training for large language models like Mixtral 8x7B. These libraries provide additional capabilities such as mixed precision training, delayed parameter initialization, and activation checkpointing to further optimize training workflows.
Source link
