GQA(Grouped-Query Attention,GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints)是分组查询注意力,GQA将查询头分成G组,每个组共享一个Key 和 Value 矩阵。GQA-G是指具有G组的grouped-query attention。GQA-1具有单个组,因此具有单个Key 和 Value,等效于MQA。而GQA-H具有与头数...
而Group Attention的思想是:在进行Multi-Head多头映射时,保留Q矩阵映射前后矩阵数据量不变的特性,但是对K和V进行分组缩放。如下图所示: 沿用前面的4个Head头的注意力机制流程,此时如果使用Group Attention,并且分组数为2时(也就是每两个分一组)。这样一来,针对4个Head的Q矩阵,原本需要4对K、V,但是我们人为将两...
group attention controlfield trialhuman-robot interactionA humanoid robot can support people in a real environment by interacting with them through human-like body movements, such as shaking hands, greeting, and pointing. In real environments, a robot often interacts with groups of people to provide...
简介:YOLO目标检测专栏探讨了Transformer在视觉任务中的效能与计算成本问题,提出EfficientViT,一种兼顾速度和准确性的模型。EfficientViT通过创新的Cascaded Group Attention(CGA)模块减少冗余,提高多样性,节省计算资源。在保持高精度的同时,与MobileNetV3-Large相比,EfficientViT在速度上有显著提升。论文和代码已公开。CGA通过...
we propose an attention-based local region merging method Group Attention Transformer (GA-Trans), which evaluates the importance of each patch by using the self-attention weight inside the Transformer, and then aggregates adjacent high weight attention blocks into groups, then randomly select groups ...
Multi Query Attention和 Group Query Attention的介绍和原理 多查询注意力(Multi Query Attention，MQA)和分组查询注意力(Group Query Attention，GQA)是在近年来对Transformer模型的改进中引起关注的新技术。MQA最早于2019年的论文《Fast Transformer Decoding: One Write-Head is All You Need》中提出，旨在解决...
Video Super-resolution with Temporal Group Attention Takashi Isobe1,2†, Songjiang Li2, Xu Jia2∗, Shanxin Yuan2, Gregory Slabaugh2, Chunjing Xu2, Ya-Li Li1, Shengjin Wang1∗, Qi Tian2 1Department of Electronic Engineering, Tsinghua University 2Noah's Ark Lab, Huawei Techn...
that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also...
视频超分:TGA(Video Super-resolution with Temporal Group Attention),程序员大本营,技术文章内容聚合第一站。
这就有了Multi-Query Attention(MQA),即query的数量还是多个,而keys和values只有一个,所有的query共享一组。这样KV Cache就变小了。 GQA 但MQA的缺点就是损失了精度,所以研究人员又想了一个折中方案:不是所有的query共享一组KV,而是一个group的guery共享一组KV,这样既降低了KV cache,又能满足精度。这就有了...