于是MQA(Multi query attention)和GQA(Grouped query attention)就应运而生,那二者区别是什么呢? 还是回到第一张图,其实很简单,主要思想就是:将K、V共享 MQA:Multi-head attention中的所有Q保留,但仅共用一对K、V GQA:对原来Multi-head attention进行分组,各组中的Q共用一对K、V 用GQA原文: GQA-1等价于MQA...
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...
Grouped-Query Attention (GQA)原理及代码介绍---以LLaMa2为例介绍了Grouped-query attention(GQA)、Multi-head attention(MHA)、Multi-queryattention(MQA)等代码链接:https://github.com/facebookresearch/llama论文链接:https://arxiv.org, 视频播放量 5368、弹幕量 1
MultiQueryAttention (MQA) [Used in Falcon LLM] and GroupedQueryAttention (GQA) [Used in Llama 2 LLM] are alternatives to MultiHeadAttention (MHA) but they are a lot faster. Here's the speed comparison in my naive implementation, ===...
MultiQueryAttention (MQA) [Used in Falcon LLM] and GroupedQueryAttention (GQA) [Used in Llama 2 LLM] are alternatives to MultiHeadAttention (MHA) but they are a lot faster. Here's the speed comparison in my naive implementation, === TensorFlow - GPU === Attention : 0.004 sec Multi...
https://www.youtube.com/watch?v=Mn_9W1nCFLo Full explanation of the LLaMA 1 and LLaMA 2 model from Meta, including Rotary Positional Embeddings, RMS Normalization, Multi-Query Attention, KV-Cache, Grouped Multi-Query Attention (GQA), the SwiGLU Activation function and more! Chapters 00:00...
根据GQA的定义，GQA-1等同于MQA，即所有Multi-head attention共享一对K、V，而GQA-H等同于传统的MHA，即保持原Multi-head attention数量不变。由此，GQA介于MQA与MHA之间，旨在通过更灵活的共享策略，实现更高的推理效率与更低的内存消耗。相较于MQA，GQA的提出得益于实验结果的验证，其展现出优于MQA的...
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 ...
MAG-Net: Multi-fusion network with grouped attention for retinal vessel segmentationdoi:10.3934/mbe.2024086Yun JiangJie ChenWei YanZequn ZhangHao QiaoMeiqi WangMathematical Biosciences & Engineering