GQA(Grouped-Query Attention,GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints)是分组查询注意力,GQA将查询头分成G组,每个组共享一个Key 和 Value 矩阵。GQA-G是指具有G组的grouped-query attention。GQA-1具有单个组,因此具有单个Key 和 Value,等效于MQA。而GQA-H具有与头数...
这就有了Multi-Query Attention(MQA),即query的数量还是多个,而keys和values只有一个,所有的query共享一组。这样KV Cache就变小了。 GQA 但MQA的缺点就是损失了精度,所以研究人员又想了一个折中方案:不是所有的query共享一组KV,而是一个group的guery共享一组KV,这样既降低了KV cache,又能满足精度。这就有了...
MQA的原理很简单，它将原生Transformer每一层多头注意力的Key线性映射矩阵、Value线性映射矩阵改为该层下所有头共享，也就是说K、V矩阵每层只有一个。举例来说，以ChatGLM2-6B为例，一共28层，32个注意力头，输入维度从4096经过Q、K、V矩阵映射维度为128，若采用原生多头注意力机制，则Q、K、V矩阵各有28×32...
文字代码解读: https://bruceyuan.com/hands-on-code/hands-on-group-query-attention-and-multi-query-attention.html GitHub 链接: https://github.com/bbruceyuan/AI-Interview-Code 可以直接跑的 notebook: https://openbayes.com/console/bbruceyuan/containers/RhWOr6vTLN4 学习过程中需要用 GPU 的同学...
77、Llama源码讲解之GroupQueryAttention和KV-cache deep_thoughts· 7-5 358406:47 IGC #[7]2 - Points Incremental Rewritten (2024.7.8) -Finitition-· 7-11 2927523:50 【空间的律动】批量插值工具箱Batch Interpolation v0.1.2使用说明 空间的律动· 2021-3-27 1746238:58:19 Applied Group Theory (Spri...
🐛 Describe the bug Hi AMD Team, On MI300X pytorch nightly grouped query attention is running into numeric errors. I have confirmed on H100 that this script does not have numeric errors. Can you look into this & potentially add an numeric...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [ROCm] sdpa group query attention bf16 numeric error · pytorch/pytorch@d21a25c
Such outsourcing of data and computation has received a lot of attention in recent years, partly due to the increasing availability of cloud computing. Outsourcing makes a DSMS service, especially computation of expensive functions on high volume streams, more affordable for parties with limited ...
In most real life networks such as social networks and biology networks, a node often involves in multiple overlapping communities. Thus, overlapping community discovery has drawn a great deal of attention and there is a lot of research on it. However, most work has focused on community ...
我们在每组 query 内部执行 self-attention 操作 (参数是共享的),然后每一组 query 输入到 decoder 的剩余部分。在标签分配时,我们对每一组应用一对一标签分配算法,这样每个 ground truth 会被分配给 K 个 positive queries。在测试的时候,只有第一组 query 被保留 (或任选一组保留,每一组的结果都差不多),...