- **解码器**:同样由N个相同的层堆叠组成。每一层包含三个子层,分别是掩码多头自注意力机制(Masked Multi - Head Self - Attention)、编码器-解码器注意力机制(Encoder - Decoder Attention)和前馈神经网络(Feed - Forward Network,FFN)。 核心机制 - **自注意力机制**:是Transformer的
Transformer related optimization, including BERT, GPT - FasterTransformer/fastertransformer/cuda/masked_multihead_attention.cu at v4.0 · NVIDIA/FasterTransformer
Specifically, inspired by the Masked Language Modeling (MLM) in NLP, we propose a masked token strategy based on the multi-head self-attention map, which dynamically masks some tokens of local patches without damaging the crucial structure for self-supervised learning. More importantly, the ...
🐛 Describe the bug Problem description The forward method of TransformerEncoderLayer provides an argument to pass in a mask to zero specific attention weights. However, the latter has no effect. Here is a minimal script to reproduce. Not...
Semantic SegmentationMapillary valMask2Former (Swin-L, multiscale)mIoU64.7# 3 Compare Semantic SegmentationMS COCOMaskFormer (Swin-L, single-scale)mIoU64.8# 6 Compare Semantic SegmentationMS COCOMask2Former (Swin-L, single-scale)mIoU67.4# 4 ...
Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Kaleido-BERT • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Vision Transformer...
Longer sequences are disproportionately expensive because attention is quadratic to the sequence length. In other words, a batch of 64 sequences of length 512 is much more expensive than a batch of 256 sequences of length 128. The fully-connected/convolutional cost is the same, but the attenti...