seq_length, embed_size)returnout@self.fc_out# 最终输出# 示例使用embed_size=128# 嵌入维度heads=8# 头的数量num_groups=4# 组的数量gqa=GroupedQueryAttention(embed_size,heads,num_groups)# 假设输入是 (批次大小, 序列长度, 嵌入维度)x=np.random.rand(64,10,embed_size)# 批次大小为64...
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为什么还提出GQA: 因为实验结果表明比MQA效果好...
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
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 attention across various query groups, allowing for more comprehensive processing and analysis. In...
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 ...
See:attention.py Intended to be a drop-in replacement forF.scaled_dot_product_attentionwith support for GQA. NOTE: The built-inF.scaled_dot_product_attentionwill bemuchfaster when you'renotusing grouped queries -- especially fortorch>=2.0, which usesflash attentionunder the hood. However,this...
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...
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...