4. 代码实现 以下是使用Python和NumPy实现Grouped-Query Attention的示例。 importnumpyasnpclassGroupedQueryAttention:def__init__(self,embed_size,heads,num_groups):self.heads=headsself.embed_size=embed_sizeself.num_groups=num_groupsself.head_dim=embed_size//headsassert(self.head_dim*heads==embed_size...
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...
GQA(Grouped Query Attention) 多头注意力在解码、做预测下一个词的任务的时候性能不佳。因为每一个token在算多头注意力的时候都需要之前所有token已经产生的K、V向量来构成KV矩阵去计算,而之前所有token的Q向量都是不需要的(Q向量只用于计算自己的输出)。 但K与V矩阵都是中间结果而不是模型的权重,真正有用的是...
In recent years,grouped query attention (GQA) What is grouped query attention (gqa)? Grouped query attention (GQA) is a fundamental concept in AI that pertains to the ability of models to focus on different groups of queries simultaneously. Essentially, GQA enables AI models to allocate attentio...
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
jainapurva added a commit to jainapurva/pytorch that referenced this pull request Aug 5, 2024 Grouped Query Attention (pytorch#132689) … 2f9dcfd Contributor facebook-github-bot commented Aug 6, 2024 This pull request was exported from Phabricator. Differential Revision: D60772086jain...
The key difference between Implementation A and B that enables Grouped Query Attention is having separate n_kv_heads and n_heads arguments. In Implementation B, n_kv_heads allows having fewer key/value projections than query projections. This enables queries to be grouped into fewer key/value he...
MQA中，所有Multi-head attention的Q保持独立，但共用一对K、V，以此提升并行计算效率。GQA则进一步将Multi-head attention进行分组，每个组内的Q共用一对K、V，以此在保留并行计算优势的同时，进一步优化内存使用。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...
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...