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),"Embedding ...
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
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也就是Multi-head attention只分了一组,共享一对K、V GQA-H等价于MHA也就是Multi-head attention分成了H组(也就是Multi-head attention数量,原来的数量),相当于没变,所以说GQA是介于MQA和MHA之间 有了MQA为什么还提...
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)。
Support for LLaMA-2 70B with Grouped-Query Attention OpenMOSS/CoLLiE#91 Open missflash commented Jul 29, 2023 Hi, I think this image is a good summary of GQA: As far as I understand GQA reduces cache sizes for keys and values by `n_heads / n_kv_heads` times. Because they have...
Discover a Comprehensive Guide to grouped query attention gqa: Your go-to resource for understanding the intricate language of artificial intelligence.
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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...
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...