GQA就是把多头注意力机制按照某一个数值将头Group起来,这样可以减少计算、减少缓存。 Q每次都要计算新的,因此没有Qcache,用完即抛
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...
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...
GQA-1:一个单独的组,等同于 Multi-Query Attention (MQA)。 GQA-H:组数等于头数,基本上与 Multi-Head Attention (MHA) 相同。 GQA-G:一个中间配置,具有G个组,平衡了效率和表达能力。 具体来说,GQA通过分组的方式,减少了需要缓存的键和值的数量,从而减少了内存的使用,同时由于不是所有头都共享键和值,它能...
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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
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...
Then I explained the concept of GQA and asked it for the parts enabling GQA: 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 ...
grouped-query-attention-pytorch (Unofficial) PyTorch implementation of grouped-query attention (GQA) fromGQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints Includes: To do: Fine-tuning code for T5 GQA models
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...