在本小节,我们将逐步分析复现Position Wise Feed Forward每行/块代码所起的作用: 3.1 初步思考 根据原文,我们实现FFN,需要两个线性变换,并在其中插入一次ReLU激活函数,那这样就很清晰明了了。 3.2 初始化 按照我们最先思考的,写好传入的参数,计算出均值和方差 def __init__(self, d_model, hidden, drop_prob...
In this paper, we propose the first hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and the position-wise feed-forward network (FFN) ResBlock, which are the two most complex layers in the Transformer. Firstly, an efficient method is introduced to...
一句话概括:在Transformer模型中加入相对位置表示,可以提升翻译结果的质量。 Transformer:采用encoder-decoder框架 encoder里面有多层,每一层包括两个子层 self-attention 和 FFN(a position-wise feed-forward layer),子层之间通过 layer normalization 连接,层与层之间通过 residua... ...
In practice, the embedding matrix multiplication can be implemented by two element-wise multiplication for lower memory footprint. The RoPE uses the form of absolute embedding but can capture relative positional relations. This approach is compatible with linearized attention in Section 4.2. View articl...
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[39] used the signal strength difference between pairwise APs, which are called difference of signal strength (DIFF), to reduce the impact of using heterogeneous smartphones. Hossain et al. [40] proposed an enhanced method called signal strength difference (SSD), which selected a DIFF ...