导入如果你看GPT系列的论文,你学习到的self-attention是 Multi-Head Attention(MHA)即多头注意力机制, MHA 包含h个Query、Key 和 Value 矩阵,所有注意力头(head)的 Key 和 Value 矩阵权重不共享。这个机制已经…
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...
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Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛关注...
简介 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Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛...
多查询注意力(MultiQuery Attention,MQA)和分组查询注意力(GroupQueryAttention,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《FastTransformer Decoding: One Write-Head is All YouNeed》中提出,旨在解决Transformer增量推理阶段效率低下的问题。虽然当时并没有引起广泛关注,但随着近...
标准的Transformer是用于序列到序列的NLP任务,它包含编码器和解码器,两者都由多头注意层(multi-head attention layer)和前馈网络(feed-forward network)组成。多头注意力通过比较一个特征和其他特征之间的两两相似性来计算权重。 简而言之,首...
Likewise, an Attentive Bi-Directional GRU does splendidly when trying to model long-term user behaviors while the Collaborative Multi Head Attention Mechanism evaluates the effect of item attributes on user preferences. Experiments conducted on benchmark datasets demonstrate the advantages of the proposed...
In contrast to prior self- attention using the same feature for all heads, CGA feeds each head with different input splits and cascades the out- put features across heads. This module not only reduces the computation redundancy in multi-head attention, but a...
Projects can be managed such that the construction group build the asset, then hand over to the commissioning team for them to then systematically dismantle the plant, clean it, and then punchlist it, returning the list of rectification works back to the construction group for attention. This ...
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...