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Linear Attention Sequence Parallel (LASP): An Efficient Machine Learning Method Tailored to Linear Attention-Based Language Models
Practical Solutions and Value
Linear attention-based models are gaining popularity for their faster processing speed and comparable performance to Softmax transformers. However, the large size and longer sequence lengths of large language models (LLMs) strain contemporary GPU hardware. LASP optimizes sequence parallelism on linear transformers, enhancing parallelism efficiency and usability. It employs point-to-point (P2P) communication for efficient state exchange among GPUs and achieves significant throughput enhancement, surpassing other methods in throughput at long sequence lengths.
LASP’s key contributions include:
- A new SP strategy tailored to linear attention, enabling linear attention-based models to scale for long sequences without being limited by a single GPU.
- Sequence length-independent communication overhead, ensuring that the exchanging of linear attention intermediate states is sequence length-independent.
- GPU-friendly implementation, optimized for efficient execution on GPUs through meticulous system engineering.
- Data-parallel compatibility, ensuring practicality for large-scale distributed training.
LASP overcomes the limitations of existing SP methods on linear transformers by leveraging linear attention features to enhance parallelism efficiency and usability. It reduces communication traffic, improves GPU cluster utilization, and is compatible with batch-level DDP methods. Experiments highlight LASP’s advantages in scalability, speed, memory usage, and convergence performance compared to existing SP methods.
For more details, check out the Paper and Github. All credit for this research goes to the researchers of this project.
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