performer pytorch
1.1.4
Реализация Performer, варианта линейного преобразователя, основанного на внимании, с быстрым вниманием и подходом положительных ортогональных случайных признаков (FAVOR+).
$ pip install performer-pytorchЗатем вам необходимо выполнить следующее, если вы планируете обучать авторегрессионную модель:
$ pip install -r requirements.txtМодель языка исполнителя
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)Plain Performer, если вы работаете, скажем, с изображениями или другими модальностями.
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)Кодер/декодер – стало возможным благодаря Томасу Мелистасу
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 tokensАвтономный уровень самообслуживания с линейной сложностью по длине последовательности для замены обученных слоев самообслуживания преобразователя полного внимания.
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)Перекрестное внимание аналогично
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) Чтобы свести к минимуму операции с моделью, вы также можете просто переписать код, чтобы этап внимания выполнялся модулем FastAttention , как показано ниже.
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 В конце обучения, если вы хотите исправить матрицы проекции, чтобы модель выдавалась детерминированно, вы можете вызвать следующую команду:
model . fix_projection_matrices_ ()Теперь ваша модель будет иметь фиксированные матрицы проекций на всех слоях.
@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 }
}
