KTransformers innovatively implements an intelligent sparse attention framework that effectively solves the memory bottleneck problem in large model inference. The technology can selectively process key information blocks in the input sequence through the block sparse attention mechanism, saving more than 50% of memory occupation. Compared with the traditional full-attention mechanism, this innovative design is especially suitable for deploying large language models in environments with limited computational resources.
In terms of specific implementation, the framework supports enabling the sparse attention module by simple configuration file modification: just add attention:type: sparse configuration item in config.yaml to activate the function. The system will automatically optimize the attention computation process to significantly improve the computation efficiency while keeping the model accuracy unchanged.
The breakthrough of this technology lies in its realization of efficient decoding in a CPU environment, which enables devices that do not have a professional GPU to run large-scale language models. Test data shows that on the Intel Xeon processor platform, the inference speed can be increased by 3-5 times after starting sparse attention, which opens up the possibility of applying large models in new types of scenarios such as edge computing.
This answer comes from the articleKTransformers: Large Model Inference Performance Engine: Extreme Acceleration, Flexible EmpowermentThe
