print(attn_weight.shape) # torch.Size([6, 2, 3, 3]) 3、GroupedQueryAttention(GQA) MHA、GQA、MQA 示意图 GQA相较于MHA的主要改动在于多个 Query 共享一个 Key-Value 组。 例如,在处理一个文本序列时,将查询头分组,组内共享键和值矩阵。例如,对于 8 头注意力,如果分为 4 组,那么每组 2 个头共享...
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...
为了减少这个内存负担，后来又发展出了两种优化技术：Multi-Query Attention（MQA）和 Grouped-Query Attention（GQA）。Multi-Query 注意力（MQA）和 Grouped-Query 注意力（GQA）下面这张图比较了原版 Multi-Head Attention（MHA）、Grouped-Query Attention（GQA）[10] 和 Multi-Query Attention（MQA）[9]。图 3....
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, ===...
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, === TensorFlow - GPU === Attention : 0.004 sec Multi...
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。
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...
We achieve this by using Position-sensitive Multi-scale attention and Grouped queries. First, to better fuse the multi-scale features, we propose a Position-sensitive Multi-scale attention. By incorporating a spatial sampling strategy into deformable attention, we can further improve the performance ...
multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value ...
Multi-head attention consists of multiple attention layers (heads) in parallel with different linear transformations on the queries, keys, values and outputs. Multi-query attention is identical except that the different heads share a single set of keys a