Transformer Feed-Forward Layers Are Key-Value Memories1. Introduction 之前大部分研究关注的是self-attention,而FF层占据了模型的 \frac{2}{3} 的参数(对于每一层,self-attention的参数量为 4 \cdot d^2 ,…
2021. Transformer feed-forward layers are key-value memories. In Proceedings of EMNLP, pages 5484–5495. Giampiccolo et al. (2007) Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. The third PASCAL recognizing textual entailment challenge. In Proceedings of TEP, pages...
action=detail&id=2630^abTransformer Feed-Forward Layers Are Key-Value Memories https://arxiv.org/abs/2012.14913
Transformer Feed-Forward Layers Are Key-Value Memories Knowledge Neurons in Pretrained Transformers ... 问题来了,如果FFN存储着Transformer的knowledge,那么注定了这个地方不好做压缩加速: FFN变小意味着model capacity也变小,大概率会让整体performance变得很差。我自己也有过一些ViT上的实验 (相信其他人也做过),...
Transformer Feed-Forward Layers Are Key-Value Memories[8] Knowledge Neurons in Pretrained Transformers[9] ... 问题来了,如果FFN存储着Transformer的knowledge,那么注定了这个地方不好做压缩加速: FFN变小意味着model capacity也变小,大概率会让整体performance变得很差。我自己也有过一些ViT上的实验 (相信其他人也...
This repository includes the accompanying code for the paper "Transformer Feed-Forward Layers Are Key-Value Memories". Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. EMNLP, 2021. The code is built upon the fairseq framework, and includes changes at the core modules that allow extrac...
接下来我们看看Transformer的一个核心特性,在这里输入序列中每个位置的单词都有自己独特的路径流入编码器。在自注意力层中,这些路径之间存在依赖关系。而前馈(feed-forward)层没有这些依赖关系。因此在前馈(feed-forward)层时可以并行执行各种路径。 位置编码操作: ...
3、线性层(Linear Layers) Query,Key,Value实际上是三个独立的线性层。每个线性层都有自己独立的权重。 输入数据与三个线性层分别相乘,产生Q、K、V。 注意力模块将其查询Query、键Key和值Value的参数矩阵进行N次拆分,并将每次拆分独立通过一个单独的注意力头。
ff_output = self.feed_forward(x) x = x + self.dropout2(ff_output) x = self.layer_norm2(x) return x class Encoder(nn.Module): def __init__(self, input_size, d_model, n_heads, d_ff, n_layers): super(Encoder, self).__init__() ...
transformer = Transformer(src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_len, dropout) # 生成随机示例数据 src_data = torch.randint(1, src_vocab_size, (5, max_len)) # (batch_size, seq_length)