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EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism (2312.04916v3)

Published 8 Dec 2023 in cs.LG, cs.AI, and cs.DC

Abstract: We present EE-LLM, a framework for large-scale training and inference of early-exit LLMs. While recent works have shown preliminary evidence for the efficacy of early exiting in accelerating LLM inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs by supporting their training and inference with massive 3D parallelism. Built upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and performance optimizations tailored to early exiting, including a lightweight method that facilitates backpropagation for the early-exit training objective with pipeline parallelism, techniques of leveraging idle resources in the original pipeline schedule for computation related to early-exit layers, and two approaches of early-exit inference that are compatible with KV caching for autoregressive generation. Our analytical and empirical study shows that EE-LLM achieves great training efficiency with negligible computational overhead compared to standard LLM training, as well as outstanding inference speedup without compromising output quality. To facilitate further research and adoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM.

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Authors (5)
  1. Yanxi Chen (21 papers)
  2. Xuchen Pan (12 papers)
  3. Yaliang Li (117 papers)
  4. Bolin Ding (112 papers)
  5. Jingren Zhou (198 papers)
Citations (18)
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Summary

An Overview of "EE-LLM: Large-Scale Training and Inference of Early-Exit LLMs with 3D Parallelism"

The paper "EE-LLM: Large-Scale Training and Inference of Early-Exit LLMs with 3D Parallelism" presents a sophisticated framework for advancing the training and inference of LLMs through early-exit paradigms. This research focuses on mitigating the computational and energy-intensive nature of LLMs by leveraging early-exit strategies, accelerating inference without compromising accuracy, and implementing 3D parallelism for large-scale deployment.

Main Contributions

The paper effectively tackles the hurdles in training and inference with early-exit LLMs by proposing a series of algorithmic innovations. These include:

  1. Backpropagation Through Pipeline Stages: The authors introduce an innovative method to support backpropagation in a pipeline-parallel setup across various exits. This is crucial as existing parallelism frameworks like Megatron-LM don't natively support cross-pipeline loss aggregation required for early-exit models.
  2. Efficiency Optimizations: EE-LLM includes several performance optimizations that minimize the computational overhead introduced by early-exit layers. The paper identifies and efficiently utilizes idle computational resources created due to pipeline bubbles, balancing workload distribution across pipeline stages, which is crucial for enhancing training and memory efficiency.
  3. Inference with KV Caching Compatibility: Two methods are devised to resolve conflicts between early-exit inference and key-value (KV) caching, both critical for autoregressive sequence generation tasks. The first employs a novel form of pipeline parallelism that allows for concurrent token generation, and the second utilizes KV recomputation to maintain speed and consistency.
  4. Model Architecture Flexibility: EE-LLM offers flexibility in configuring early-exit layer structures and their distribution across the model pipeline, providing researchers the tools to balance complexity and computational savings.

Analytical and Empirical Insights

The research presents both analytical and empirical analyses to support the claim of achieving higher training efficiency with minimal overhead. Empirical results confirm that training time increases marginally with the introduction of early-exit layers, while peak memory usage can remain constant or negatively impacted when optimally positioned stages are utilized. This is significant because it means early-exit models can scale up to sizes comparable to conventional LLMs within the constraints of existing computing resources.

In terms of inference, the development of a pipeline-based method ensures that the advantages of early-exit models can be fully realized without delaying future token generation due to missing KV caches. Analysis reveals that this method provides substantial speedup in sequence generation with minimal impact on final output quality.

Implications and Future Directions

EE-LLM extends the application scope of 3D parallelism by incorporating early-exit logic into the training and deployment of LLMs. The implications of such advancements are broad, suggesting that massive LLMs, previously limited by computational resource constraints, can be trained and deployed more efficiently.

The theoretical and practical contributions provide a foundation for future exploration into optimizing various checkpoints in model training and inference. The advanced support for variable configurations within early-exit frameworks opens additional avenues for exploring adaptive computation in real-time, allowing for dynamic model scalability. Moreover, future explorations could further integrate early-exit mechanisms with other conditional computation strategies like sparse mixtures of experts to amplify efficiency gains.

The EE-LLM framework bridges a critical gap in LLM scaling by aligning early-exit benefits with 3D parallelism, emphasizing both the immediate and broader adoption potential of early-exit strategies in the AI community. These insights and methods potentially redefine the computational economics of deploying large-scale LLMs in diverse applications.

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