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...
下图能很好的反映Multi-Head/Multi-Query/Grouped-Query Attention之间的区别。 1.Prefill Phase Prefill阶段的计算过程如下,与Multi-Head Attention相比K和V少了一个维度。其实到这边已经也可以预料到Muti-Head Attention在预填充阶段是肯定不会memory bandwidth bound的,但我们这边还是会计算一下。 计算复杂度为Θ(bnd...
MultiQueryAttention (MQA) [Used in Falcon LLM] and GroupedQueryAttention (GQA) [Used in Llama 2 LLM] are alternatives to MultiHeadAttention (MHA) but they are a lot faster. Here's the speed comparison in my naive implementation, ===...
Probably easiest to just write GroupedQueryAttention, and consider MultiQueryAttention a special case of it. We can expose MultiQueryAttention, as subclass of GroupedQueryAttention that sets a single init value num_key_value_heads=1 on the base class. Somewhat similar to our AdamW class with we...
一般的multi head attention 的qkv的头的数量都一样，而multi query attention的q的头数量保持不变，k...
Homologous recombination is a robust, broadly error-free mechanism of double-strand break repair, and deficiencies lead to PARP inhibitor sensitivity. Patients displaying homologous recombination deficiency can be identified using ‘mutational signatures
Related task:常规思路(自动驾驶+路标识别;query classification+web search;坐标预测+物体识别;duration+frequency) Adversarial:在domain adaption,相关的任务可能无法获取,可以使用对抗任务作为negative task(最大化training error),比如辅助任务为预测输入的domain,则导...
Related task:常规思路(自动驾驶+路标识别;query classification+web search;坐标预测+物体识别;duration+frequency) Adversarial:在domain adaption,相关的任务可能无法获取,可以使用对抗任务作为negative task(最大化training error),比如辅助任务为预测输入的domain,则导致主任务模型学习的表征不能区分不同的domain。
与Multi-head Attention相比,Query还是多个头,KV变成一个头,节省了很多计算量。模型的精度会稍微降低一点,但是推理速度会快很多。 Grouped-query Attention是multi-head和multi-query方案的折中。模型精度高于multi-query,速度优于multi-head。LLaMA2在34B和70B的模型上使用了Grouped-Query Attention。
The open source implementation of the multi grouped query attention by the paper "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints" - kyegomez/MGQA