InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
📰 ArXiv cs.AI
Learn how InfiniPipe enables efficient variable-length long-context LLM training using elastic pipeline parallelism, reducing communication overhead and memory consumption
Action Steps
- Implement InfiniPipe to enable elastic pipeline parallelism in LLM training
- Configure partitioning granularity to balance communication overhead and memory consumption
- Apply sequence packing and token-level splitting to optimize memory usage
- Test the effectiveness of InfiniPipe in reducing training time and improving model performance
- Compare the results with existing pipeline parallelism schemes
Who Needs to Know This
ML engineers and researchers working on large language models can benefit from this technique to improve training efficiency and scalability
Key Insight
💡 Elastic pipeline parallelism can significantly reduce communication overhead and memory consumption in variable-length long-context LLM training
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🚀 InfiniPipe enables efficient LLM training with elastic pipeline parallelism! 🤖
Key Takeaways
Learn how InfiniPipe enables efficient variable-length long-context LLM training using elastic pipeline parallelism, reducing communication overhead and memory consumption
Full Article
Title: InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
Abstract:
arXiv:2509.21275v3 Announce Type: replace-cross Abstract: Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) reduces this cost, but its effectiveness hinges on partitioning granularity. Batch-level PP employing sequence packing exhibits high memory consumption in long-context scenarios, whereas token-level PP splitting sequences into slices alleviates memory overhead but
Abstract:
arXiv:2509.21275v3 Announce Type: replace-cross Abstract: Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) reduces this cost, but its effectiveness hinges on partitioning granularity. Batch-level PP employing sequence packing exhibits high memory consumption in long-context scenarios, whereas token-level PP splitting sequences into slices alleviates memory overhead but
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