为了充分理解LASP的思路,让我们先回顾下传统Softmax Attention的计算公式:O=softmax((QK^T)⊙M)V,其Q, K, V, M, O分别为Query, Key, Value, Mask和Output矩阵,这里的M在单向任务(如GPT)中是一个下三角的全1矩阵,在双向任务(如BERT)中则可以忽略,即双向任务没有Mask矩阵。我们下面将LASP拆为四点进行解...
首先，把bert换成其他的特征提取器，例如cnn，我们知道前面增加模型的深度，是可能过拟合，效果反而差的...
3.3 复杂度优化实践 Flash Attention:通过分块计算和IO优化,将内存复杂度从O(n2)O(n2)降至O(n)O(n) Sparse Attention:使用局部窗口(如Longformer的滑动窗口)或随机模式(如Reformer) 低秩近似:Linformer将K,V投影到低维空间,复杂度从O(n2)O(n2)降至O(nk)O(nk) 四、架构演进与未来方向 Transformer核心组件...
In contrast, linear attention provides a far more efficient solution by reducing the complexity to linear levels. However, compared to Softmax attention, linear attention often experiences significant performance degradation. Our experiments indicate that this performance drop is due to the low-rank ...
Linear Attention Conformer, an evolved iteration of the conformer architecture. Shifted Linear Attention Conformer adopts shifted linear attention as a scalable alternative to softmax attention. We conducted a thorough analysis of the factors constraining the efficiency of linear attention. To mitigate ...
BERT focuses on using a new masked language model (MLM) to train a bidirectional transformer for creating deep bidirectional language representations. The coding layer of this mechanism uses a multi-head self-attention approach to process both left and right contexts simultaneously, allowing for parall...
Knowledge Distillation:和DistillBERT一样,利用学生模型去学习教师模型的分布预测; Sparse Attention:只计算对角线部分的注意力权重; 该技术通过在上下文映射矩阵P中添加稀疏性来提高自我注意的效率。例如,sparse transformer只计算矩阵P的对角线附近的Pij(而不是所有的Pij)。同时,block-wise self-attention将P划分为多个...
论文阅读《Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks》,程序员大本营,技术文章内容聚合第一站。
BERT-based intent and slots detector for chatbots. - bert-intent-slot-detector/models.py at master · Linear95/bert-intent-slot-detector
an attention neural network configured to perform the machine learning task, the attention neural network comprising a plurality of attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer, the attention sub-layer configured to:receive an input sequence ...