魔改Attention大集合

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2020-08-29 08:09

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来源丨NewBeeNLP
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如何对attention进行高效改进?本文盘点了相关论文,并梳理出它们的引用量、代码实现、算法复杂度和关键点,方便对比使用。

前几天逛github刷到一个awesome-fast-attention大列表,整理了一系列关于attention的高效改进文章,包括论文、引用量、源码实现、算法复杂度以及关键亮点。
Github地址:
https://github.com/Separius/awesome-fast-attention

Efficient Attention

Paper (引用量)源码实现复杂度AutoRegressiveMain Idea
Generating Wikipedia by Summarizing Long Sequences[1] (208)memory-compressed-attention[2]
compresses key and value + blocked attention
CBAM: Convolutional Block Attention Module[3] (677)attention-module[4] combines the SE attention with a per pixel(local) weight
CCNet: Criss-Cross Attention for Semantic Segmentation[5] (149)CCNet[6]each pixel attends to its row and column simultaneously
Efficient Attention: Attention with Linear Complexities[7] (2)efficient-attention[8]Softmax(Q)*(Softmax(K^T)*V)
Star-Transformer[9] (24)fastNLP[10]uses a relay(global) node and attends to/from that node
Generating Long Sequences with Sparse Transformers[11] (139)torch-blocksparse[12]sparse block based attention
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond[13] (96)GCNet[14] squeeze and excitation with an attention pooling (instead of a GAP)
SCRAM: Spatially Coherent Randomized Attention Maps[15] (1)-uses PatchMatch to find close keys
Interlaced Sparse Self-Attention for Semantic Segmentation[16] (13)IN_PAPERcombination of a short length and then long range(dilated) attention
Permutohedral Attention Module for Efficient Non-Local Neural Networks[17] (2)Permutohedral_attention_module[18]uses permutohedral lattice approximation algorithm to approximate the attention output
Large Memory Layers with Product Keys[19] (28)XLM[20]search for nearest neighbor keys
Expectation-Maximization Attention Networks for Semantic Segmentation[21] (38)EMANet[22]applys expectation maximization to cluster keys into k clusters
Compressive Transformers for Long-Range Sequence Modelling[23] (20)compressive-transformer-pytorch[24]compresses distant tokens instead of just stop_grad() ing them, more efficient version of transformerXL
BP-Transformer: Modelling Long-Range Context via Binary Partitioning[25] (8)BPT[26]attends to distant tokens coarsely and attends to close tokens in a more fine-grained manner
Axial Attention in Multidimensional Transformers[27] (5)axial-attention[28]apply attention on each axis separately
Reformer: The Efficient Transformer[29] (69)trax[30]uses LSH to find close keys
Transformer on a Diet[31] (2)transformer-on-diet[32]
dilated transformer like wavenet
Sparse Sinkhorn Attention[33] (4)sinkhorn-transformer[34]
uses a cost matrix to limit attention between buckets
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection[35] (1)-
learns the q, k connections == dynamically creates a sparse attention matrix
Efficient Content-Based Sparse Attention with Routing Transformers[36] (11)routing-transformer[37]
computes attention with same-cluster tokens (computed by online k-means)
Longformer: The Long-Document Transformer[38] (15)longformer[39]
global + blocked attention
Neural Architecture Search for Lightweight Non-Local Networks[40] (2)AutoNL[41]
computes Q(KV) and also down samples q, k, v both in spatial and channel dimensions
ETC: Encoding Long and Structured Data in Transformers[42] (2)-
combines global attention (star transformer with multiple global tokens) with local attention
Multi-scale Transformer Language Models[43] (1)IN_PAPER
UNet like + retina attetion is something close to BP-Transformer
Synthesizer: Rethinking Self-Attention in Transformer Models[44] (5)-
does not compute pairwise interactions
Jukebox: A Generative Model for Music[45] (9)jukebox[46]
better attention patterns from Sparse Transformer
GMAT: Global Memory Augmentation for Transformers[47] (0)gmat[48]
adds global tokens
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers[49] (0)google-research[50]
calculate an unbiased stochastic approximation of the attention matrix
Hand-crafted Attention is All You Need? A Study of Attention on Self-supervised Audio Transformer[51] (0)-
does not compute pairwise interactions and uses fixed mask patters
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention[52] (1)fast-transformers[53]
uses phi(q)(phi(k)v) and also improves the sequential sampling step
Linformer: Self-Attention with Linear Complexity[54] (3)linformer-pytorch[55]
project key and value from nd
Real-time Semantic Segmentation with Fast Attention[56] (0)-
l2_norm(q)*(l2_norm(k)*v)
Fast Transformers with Clustered Attention[57] (0)fast-transformers[58]
groups queries together with LSH
Big Bird: Transformers for Longer Sequences[59] (0)-
ETC with random connections


参考资料
[1] Generating Wikipedia by Summarizing Long Sequences: https://arxiv.org/abs/1801.10198v1
[2]memory-compressed-attention: https://github.com/lucidrains/memory-compressed-attention
[3] CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521v2
[4] attention-module: https://github.com/Jongchan/attention-module 
[5] CCNet: Criss-Cross Attention for Semantic Segmentation: https://arxiv.org/abs/1811.11721v2
[6] CCNet: https://github.com/speedinghzl/CCNet
[7] Efficient Attention: Attention with Linear Complexities: https://arxiv.org/abs/1812.01243v8
[8] Efficient-attention: https://github.com/cmsflash/efficient-attention 
[9] Star-Transformer: https://arxiv.org/abs/1902.09113v2
[10] fastNLP: https://github.com/fastnlp/fastNLP/blob/master/fastNLP/modules/encoder/star_transformer.py
[11] Generating Long Sequences with Sparse Transformers: https://arxiv.org/abs/1904.10509v1
[12] torch-blocksparse: https://github.com/ptillet/torch-blocksparse
[13] GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond: https://arxiv.org/abs/1904.11492v1
[14] GCNet: https://github.com/xvjiarui/GCNet
[15] SCRAM: Spatially Coherent Randomized Attention Maps: https://arxiv.org/abs/1905.10308v1
[16] Interlaced Sparse Self-Attention for Semantic Segmentation: https://arxiv.org/abs/1907.12273v2
[17] Permutohedral Attention Module for Efficient Non-Local Neural Networks: https://arxiv.org/abs/1907.00641v2 
[18] Permutohedral_attention_module: https://github.com/SamuelJoutard/Permutohedral_attention_module 
[19] Large Memory Layers with Product Keys: https://arxiv.org/abs/1907.05242v2 
[20] XLM: https://github.com/facebookresearch/XLM 
[21] Expectation-Maximization Attention Networks for Semantic Segmentation: https://arxiv.org/abs/1907.13426v2 
[22] EMANet: https://github.com/XiaLiPKU/EMANet 
[23] Compressive Transformers for Long-Range Sequence Modelling: https://arxiv.org/abs/1911.05507v1 
[24] compressive-transformer-pytorch: https://github.com/lucidrains/compressive-transformer-pytorch
[25] BP-Transformer: Modelling Long-Range Context via Binary Partitioning: https://arxiv.org/abs/1911.04070v1
[26] BPT: https://github.com/yzh119/BPT
[27] Axial Attention in Multidimensional Transformers: https://arxiv.org/abs/1912.12180v1
[28] axial-attention: https://github.com/lucidrains/axial-attention
[29] Reformer: The Efficient Transformer: https://arxiv.org/abs/2001.04451v2
[30] trax: https://github.com/google/trax/tree/master/trax/models/reformer 
[31] Transformer on a Diet: https://arxiv.org/abs/2002.06170v1
[32] transformer-on-diet: https://github.com/cgraywang/transformer-on-diet
[33] Sparse Sinkhorn Attention: https://arxiv.org/abs/2002.11296v1
[34] sinkhorn-transformer: https://github.com/lucidrains/sinkhorn-transformer
[35] SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection: https://arxiv.org/abs/2003.09833v2 
[36] Efficient Content-Based Sparse Attention with Routing Transformers: https://arxiv.org/abs/2003.05997v1
[37] routing-transformer: https://github.com/lucidrains/routing-transformer 
[38] Longformer: The Long-Document Transformer: https://arxiv.org/abs/2004.05150v1
[39] longformer: https://github.com/allenai/longformer
[40] Neural Architecture Search for Lightweight Non-Local Networks: https://arxiv.org/abs/2004.01961v1 
[41] AutoNL: https://github.com/LiYingwei/AutoNL
[42] ETC: Encoding Long and Structured Data in Transformers: https://arxiv.org/abs/2004.08483v2 
[43] Multi-scale Transformer Language Models: https://arxiv.org/abs/2005.00581v1
[44] Synthesizer: Rethinking Self-Attention in Transformer Models: https://arxiv.org/abs/2005.00743v1
[45] Jukebox: A Generative Model for Music: https://arxiv.org/abs/2005.00341v1 
[46] jukebox: https://github.com/openai/jukebox
[47] GMAT: Global Memory Augmentation for Transformers: https://arxiv.org/abs/2006.03274v1 
[48] gmat: https://github.com/ag1988/gmat 
[49] Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers: https://arxiv.org/abs/2006.03555v1
[50] google-research: https://github.com/google-research/google-research/tree/master/performer/fast_self_attention
[51] Hand-crafted Attention is All You Need? A Study of Attention on Self-supervised Audio Transformer: https://arxiv.org/abs/2006.05174v1 
[52] Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention: https://arxiv.org/abs/2006.16236v2
[53] fast-transformers: https://github.com/idiap/fast-transformers
[54] Linformer: Self-Attention with Linear Complexity: https://arxiv.org/abs/2006.04768v3 
[55] linformer-pytorch: https://github.com/tatp22/linformer-pytorch
[56] Real-time Semantic Segmentation with Fast Attention: https://arxiv.org/abs/2007.03815v2
[57] Fast Transformers with Clustered Attention: https://arxiv.org/abs/2007.04825v1
[58] fast-transformers: https://github.com/idiap/fast-transformers
[59] Big Bird: Transformers for Longer Sequences: https://arxiv.org/abs/2007.14062v1 
[60] A Survey of Long-Term Context in Transformers: https://www.pragmatic.ml/a-survey-of-methods-for-incorporating-long-term-context/


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