arXiv:2606.30460v1 Announce Type: new Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly compute causal attention on the hybrid-context packed sequences, in a stronger sequence parallelism framework. The practical technique of packing sequences for efficiently pretraining and fine-tuning large language models causes cross-contamination problem in attention computation, which can be effectively solved when no parallelism in the sequence length dimension is
The continuous scaling of Large Language Models (LLMs) is pushing the limits of current hardware and software architectures, necessitating more efficient parallelism techniques to handle increasing sequence lengths and model sizes, especially with packed sequences.
Efficiently pretraining and fine-tuning LLMs is crucial for advancing AI capabilities and reducing the immense computational costs associated with large-scale model development, directly impacting the feasibility and accessibility of powerful AI systems.
This research introduces a method to overcome critical limitations in sequence parallelism for hybrid-context models, potentially leading to more efficient and scalable training of next-generation LLMs without sacrificing accuracy due to cross-contamination.
- · AI compute infrastructure providers
- · Large Language Model developers
- · Cloud service providers
- · AI researchers
- · Developers relying on less optimized parallelism techniques
- · Hardware manufacturers unable to adapt to new parallelism demands
Improved computational efficiency for training large AI models will accelerate their development and deployment.
Reduced training costs could democratize access to advanced AI, allowing more entities to develop and customize powerful models.
This could intensify the global competition for AI leadership as the barriers to entry for large model development are lowered, potentially impacting national compute strategies.
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Read at arXiv cs.LG