在大模型技术中,GQA(Grouped Query Attention)是一种注意力机制,它介于MHA(Multi-Head Attention)和MQA(Multi-Query Attention)之间,旨在结合两者的优点,以实现在保持MQA推理速度的同时接近MHA的精度。 MHA是一种基础的注意力机制,它通过将输入分割成多个头(heads)来并行计算注意力,每个头学习输入的不同部分,最终将...
在大模型技术中,GQA(Grouped Query Attention)是一种注意力机制,它介于MHA(Multi-Head Attention)和MQA(Multi-Query Attention)之间,旨在结合两者的优点,以实现在保持MQA推理速度的同时接近MHA的精度。 MHA是一种基础的注意力机制,它通过将输入分割成多个头(heads)来并行计算注意力,每个头学习输入的不同部分,最终将...
In the broader context of AI and attention mechanisms, there are several related terms that are relevant to the understanding of grouped query attention (GQA), including: Query-based Attention Mechanisms Multi-Head Attention Self-Attention Models Query-Group Analysis in AI These related terms contribu...
Video Semantic Segmentation via Sparse Temporal Transformer-ACM 2021-[github] We appreciate the excellent work of the authors mentioned above. Citation computer-visiontransformersegmentationsemantic-segmentationencoder-decoderinstance-segmentationmultihead-attentionself-attention...
9,SqueezeBertSelfAttention源码完整实现分析 10,SqueezeBertModule源码完整实现分析 11,SqueezeBertEncoder源码完整实现分析 12,SqueezeBertPooler源码完整实现分析 13,SqueezeBertPredictionHeadTransform源码完整实现分析 14,SqueezeBertLMPredictionHead源码完整实现分析
在大模型技术中,GQA(Grouped Query Attention)是一种注意力机制,它介于MHA(Multi-Head Attention)和MQA(Multi-Query Attention)之间,旨在结合两者的优点,以实现在保持MQA推理速度的同时接近MHA的精度。 MHA是一种基础的注意力机制,它通过将输入分割成多个头(heads)来并行计算注意力,每个头学习输入的不同部分,最终将...
contrastive learning; self-attention; data augmentation; grouped representation; unsupervised learning1. Introduction Representation learning of sentences involves learning a meaningful representation for a sentence. Most downstream tasks in natural language processing (NLP) are implemented with sentence ...
The following section will provide a detailed introduction to Grouped Vector Attention. 3.3.1. Grouped Vector Attention Traditional vector attention mechanisms suffer from a rapid increase in the number of parameters in the multi-layer perceptron (MLP) used for weight encoding, as the number of ...