performer pytorch
1.1.4
Performer 的實現，是一種基於線性注意力的變壓器變體，具有透過正正交R和特徵方法進行快速注意力的方法 (FAVOR+)。
$ pip install performer-pytorch如果您計劃訓練自迴歸模型，則必須執行以下命令
$ pip install -r requirements.txt表演者語言模型
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，如果您正在處理圖像或其他形式
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)編碼器/解碼器 - 由 Thomas Melistas 實現
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獨立的自註意力層在序列長度方面具有線性複雜度，用於替換訓練有素的全注意力變壓器自註意力層。
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)交叉注意力也是類似的
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)為了最大限度地減少模型手術，您還可以簡單地重寫程式碼，以便由FastAttention模組完成注意力步驟，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 在訓練結束時，如果您希望修復投影矩陣以使模型確定性地輸出，您可以呼叫以下命令
model . fix_projection_matrices_ ()現在您的模型將在所有圖層上具有固定的投影矩陣
@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 }
}
