Self Reproduction Code of Paper "Reducing Transformer Key-Value Cache Size with Cross-Layer Attention (MIT CSAIL) - JerryYin777/Cross-Layer-Attention
由此得出的attention scores具体描述了跨层依赖的关系,同时也量化了分层信息对查询层的重要性。 利用网络的顺序结构,提出recurrent layer attention(RLA),引入多头设计,这就是 MRLA。 大多数层更加关注同一阶段内的第一层,验证了我们回顾性检索信息的动机。 继承自基本的注意力机制,MRLA的复杂度为O(T²)其中T 代...
Moreover, a cross-layer attention module (CAM) is designed to obtain the non-local association of small objects in each layer, and further strengthen its representation ability through cross-layer integration and balance. Extensive experiments on the publicly available dataset (DIOR dataset and NWPU...
一个BasicTransformerBlock包含两个CrossAttention layer(attn1,attn2) 第一个layer只是self attention,不与condition embedding做交互 第二个才是cross embedding,当condition embedding为None时也是self-attention #此处只把一些跟access attention有关的代码放出来,其他的省略 #call by unet(sample,t, encoder_hidden_s...
The proposed module consists of two components for cross-layer feature fusion and feature refinement, respectively. The former collects rich contextual cues by fusing the features from distinct layers, while the later calculates the cross-layer attention maps and applies them with the fused features....
("bert-base-cased", "bert-base-cased")and fine-tune the model. This means especially the decoder weights have to be adapted a lot, since in the EncoderDecoder framework the model has a causal mask and the cross attention layers are to be trained from scratch. The results so far are ...
In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extracto...
Try setting the "Upcast cross attention layer to float32" option inSettings > Stable Diffusion可在WebUI的设置里Stable Diffusion栏最下方勾选开启。*需注意开启该选项有几率在出图的最后阶段报type不一致的错误,"type32 type32 type16"字样的,若要解决此报错又需要你反过来关闭Upcast cross attention layer to...
然后正式进入了EncoderLayer层的,attention的计算的部分: 这个attention的计算也就是AutoCorrelationLayer这个部分:发现这个部分相比于Transformer的attention的计算中主要有区别的就是inner_correlation这个部分。 接下来进入到了其中最麻烦的部分也就是,AutoCorrelation的计算的部分。
训练采用realflow数据集,采用train_gmflow.sh原始的训练脚本,只是二者在网络构建时,一个只用self attention,一个只用cross attention,attention采用swin transformer,6个layer 层。验证集采用flying chairs, sintel数据集 self attention版本训练时验证集上的指标 ...