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...
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。 LLaMA2的34B和70B的模型是采用了Grouped Multi-Query Attention。在 30B 模型上训练 150B tokens,发现 GQA 效果和 MHA 差不多,比 MQA 要好;在 ...
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...
[76]. The general case, called GLAV, in which queries over the source are mapped to queries over the target, has attracted a lot of attention recently, especially for data exchange. The GAV and LAV are widely implemented in polystores. For more details about query processing via schema ...
个人觉得当然可以，在自然语言处理任务中，GPT 模型通常使用注意力机制来进行建模。multi-query attention ...
二、Multi-Query Attention性能分析 为了解决上述问题,作者提出了Multi-Query Attention,改进是Key和Value都只保留一份,而不是原来h份。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...
transformerspytorchtransformerattentionattention-mechanismsoftmax-layermulti-head-attentionmulti-query-attentiongrouped-query-attentionscale-dot-product-attention UpdatedOct 7, 2023 Python Improve this page Add a description, image, and links to themulti-query-attentiontopic page so that developers can more ...
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