performer pytorch
1.1.4
Performer 的实现，是一种基于线性注意力的变压器变体，具有通过正正交R和特征方法进行快速注意力的方法 (FAVOR+)。
$ pip install performer-pytorch如果您计划训练自回归模型，则必须运行以下命令
$ pip install -r requirements.txt表演者语言模型
import torch
from performer_pytorch import PerformerLM
model = PerformerLM (
num_tokens = 20000 ,
max_seq_len = 2048 , # max sequence length
dim = 512 , # dimension
depth = 12 , # layers
heads = 8 , # heads
causal = False , # auto-regressive or not
nb_features = 256 , # number of random features, if not set, will default to (d * log(d)), where d is the dimension of each head
feature_redraw_interval = 1000 , # how frequently to redraw the projection matrix, the more frequent, the slower the training
generalized_attention = False , # defaults to softmax approximation, but can be set to True for generalized attention
kernel_fn = torch . nn . ReLU (), # the kernel function to be used, if generalized attention is turned on, defaults to Relu
reversible = True , # reversible layers, from Reformer paper
ff_chunks = 10 , # chunk feedforward layer, from Reformer paper
use_scalenorm = False , # use scale norm, from 'Transformers without Tears' paper
use_rezero = False , # use rezero, from 'Rezero is all you need' paper
ff_glu = True , # use GLU variant for feedforward
emb_dropout = 0.1 , # embedding dropout
ff_dropout = 0.1 , # feedforward dropout
attn_dropout = 0.1 , # post-attn dropout
local_attn_heads = 4 , # 4 heads are local attention, 4 others are global performers
local_window_size = 256 , # window size of local attention
rotary_position_emb = True , # use rotary positional embedding, which endows linear attention with relative positional encoding with no learned parameters. should always be turned on unless if you want to go back to old absolute positional encoding
shift_tokens = True # shift tokens by 1 along sequence dimension before each block, for better convergence
)
x = torch . randint ( 0 , 20000 , ( 1 , 2048 ))
mask = torch . ones_like ( x ). bool ()
model ( x , mask = mask ) # (1, 2048, 20000)普通表演者，如果您正在处理图像或其他形式
import torch
from performer_pytorch import Performer
model = Performer (
dim = 512 ,
depth = 1 ,
heads = 8 ,
causal = True
)
x = torch . randn ( 1 , 2048 , 512 )
model ( x ) # (1, 2048, 512)编码器/解码器 - 由 Thomas Melistas 实现
import torch
from performer_pytorch import PerformerEncDec
SRC_SEQ_LEN = 4096
TGT_SEQ_LEN = 4096
GENERATE_LEN = 512
enc_dec = PerformerEncDec (
dim = 512 ,
tie_token_embed = True ,
enc_num_tokens = 20000 ,
enc_depth = 6 ,
enc_heads = 8 ,
enc_max_seq_len = SRC_SEQ_LEN ,
dec_num_tokens = 20000 ,
dec_depth = 6 ,
dec_heads = 8 ,
dec_max_seq_len = TGT_SEQ_LEN ,
)
src = torch . randint ( 0 , 20000 , ( 1 , SRC_SEQ_LEN ))
tgt = torch . randint ( 0 , 20000 , ( 1 , TGT_SEQ_LEN ))
src_mask = torch . ones_like ( src ). bool ()
tgt_mask = torch . ones_like ( src ). bool ()
# train
enc_dec . train ()
loss = enc_dec ( src , tgt , enc_mask = src_mask , dec_mask = tgt_mask )
loss . backward ()
# generate
generate_in = torch . randint ( 0 , 20000 , ( 1 , SRC_SEQ_LEN )). long ()
generate_out_prime = torch . tensor ([[ 0. ]]). long () # prime with <bos> token
samples = enc_dec . generate ( generate_in , generate_out_prime , seq_len = GENERATE_LEN , eos_token = 1 ) # assume 1 is id of stop token
print ( samples . shape ) # (1, <= GENERATE_LEN) decode the tokens独立的自注意力层在序列长度方面具有线性复杂度，用于替换训练有素的全注意力变压器自注意力层。
import torch
from performer_pytorch import SelfAttention
attn = SelfAttention (
dim = 512 ,
heads = 8 ,
causal = False ,
). cuda ()
x = torch . randn ( 1 , 1024 , 512 ). cuda ()
attn ( x ) # (1, 1024, 512)交叉注意力也是类似的
import torch
from performer_pytorch import CrossAttention
attn = CrossAttention (
dim = 512 ,
heads = 8
). cuda ()
x = torch . randn ( 1 , 1024 , 512 ). cuda ()
context = torch . randn ( 1 , 512 , 512 ). cuda ()
attn ( x , context = context ) # (1, 1024, 512)为了最大限度地减少模型手术，您还可以简单地重写代码，以便由FastAttention模块完成注意力步骤，如下所示。
import torch
from performer_pytorch import FastAttention
# queries / keys / values with heads already split and transposed to first dimension
# 8 heads, dimension of head is 64, sequence length of 512
q = torch . randn ( 1 , 8 , 512 , 64 )
k = torch . randn ( 1 , 8 , 512 , 64 )
v = torch . randn ( 1 , 8 , 512 , 64 )
attn_fn = FastAttention (
dim_heads = 64 ,
nb_features = 256 ,
causal = False
)
out = attn_fn ( q , k , v ) # (1, 8, 512, 64)
# now merge heads and combine outputs with Wo 在训练结束时，如果您希望修复投影矩阵以使模型确定性地输出，您可以调用以下命令
model . fix_projection_matrices_ ()现在您的模型将在所有层上具有固定的投影矩阵
@misc { choromanski2020rethinking ,
title = { Rethinking Attention with Performers } ,
author = { Krzysztof Choromanski and Valerii Likhosherstov and David Dohan and Xingyou Song and Andreea Gane and Tamas Sarlos and Peter Hawkins and Jared Davis and Afroz Mohiuddin and Lukasz Kaiser and David Belanger and Lucy Colwell and Adrian Weller } ,
year = { 2020 } ,
eprint = { 2009.14794 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.LG }
} @inproceedings { kitaev2020reformer ,
title = { Reformer: The Efficient Transformer } ,
author = { Nikita Kitaev and Lukasz Kaiser and Anselm Levskaya } ,
booktitle = { International Conference on Learning Representations } ,
year = { 2020 } ,
url = { https://openreview.net/forum?id=rkgNKkHtvB }
} @inproceedings { katharopoulos_et_al_2020 ,
author = { Katharopoulos, A. and Vyas, A. and Pappas, N. and Fleuret, F. } ,
title = { Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention } ,
booktitle = { Proceedings of the International Conference on Machine Learning (ICML) } ,
year = { 2020 }
} @misc { bachlechner2020rezero ,
title = { ReZero is All You Need: Fast Convergence at Large Depth } ,
author = { Thomas Bachlechner and Bodhisattwa Prasad Majumder and Huanru Henry Mao and Garrison W. Cottrell and Julian McAuley } ,
year = { 2020 } ,
url = { https://arxiv.org/abs/2003.04887 }
} @article { 1910.05895 ,
author = { Toan Q. Nguyen and Julian Salazar } ,
title = { Transformers without Tears: Improving the Normalization of Self-Attention } ,
year = { 2019 } ,
eprint = { arXiv:1910.05895 } ,
doi = { 10.5281/zenodo.3525484 } ,
} @misc { shazeer2020glu ,
title = { GLU Variants Improve Transformer } ,
author = { Noam Shazeer } ,
year = { 2020 } ,
url = { https://arxiv.org/abs/2002.05202 }
} @misc { roy*2020efficient ,
title = { Efficient Content-Based Sparse Attention with Routing Transformers } ,
author = { Aurko Roy* and Mohammad Taghi Saffar* and David Grangier and Ashish Vaswani } ,
year = { 2020 } ,
url = { https://arxiv.org/pdf/2003.05997.pdf }
} @misc { su2021roformer ,
title = { RoFormer: Enhanced Transformer with Rotary Position Embedding } ,
author = { Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu } ,
year = { 2021 } ,
eprint = { 2104.09864 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL } ,
url = { https://arxiv.org/abs/2104.09864 }
}
