local attention

Perhatian lokal

Implementasi perhatian berjendela lokal, yang menetapkan dasar yang sangat kuat untuk pemodelan bahasa. Menjadi jelas bahwa trafo memerlukan perhatian lokal pada lapisan bawah, dan lapisan atas dicadangkan untuk perhatian global guna mengintegrasikan temuan pada lapisan sebelumnya. Repositori ini memudahkan untuk segera menggunakan perhatian jendela lokal.

Kode ini telah diuji coba di beberapa repositori, bersamaan dengan implementasi berbeda dengan perhatian jangka panjang yang jarang.

Memasang

$ pip install local-attention

Penggunaan

 import torch
from local_attention import LocalAttention

q = torch . randn ( 2 , 8 , 2048 , 64 )
k = torch . randn ( 2 , 8 , 2048 , 64 )
v = torch . randn ( 2 , 8 , 2048 , 64 )

attn = LocalAttention (
    dim = 64 ,                # dimension of each head (you need to pass this in for relative positional encoding)
    window_size = 512 ,       # window size. 512 is optimal, but 256 or 128 yields good enough results
    causal = True ,           # auto-regressive or not
    look_backward = 1 ,       # each window looks at the window before
    look_forward = 0 ,        # for non-auto-regressive case, will default to 1, so each window looks at the window before and after it
    dropout = 0.1 ,           # post-attention dropout
    exact_windowsize = False # if this is set to true, in the causal setting, each query will see at maximum the number of keys equal to the window size
)

mask = torch . ones ( 2 , 2048 ). bool ()
out = attn ( q , k , v , mask = mask ) # (2, 8, 2048, 64)

Pustaka ini juga memungkinkan perhatian lokal dalam pengaturan kueri/ruang kunci bersama (arsitektur Reformator). Normalisasi kunci, serta penyembunyian token pada dirinya sendiri, akan ditangani.

 import torch
from local_attention import LocalAttention

qk = torch . randn ( 2 , 8 , 2048 , 64 )
v  = torch . randn ( 2 , 8 , 2048 , 64 )

attn = LocalAttention (
    dim = 64 ,
    window_size = 512 ,
    shared_qk = True ,
    causal = True
)

mask = torch . ones ( 2 , 2048 ). bool ()
out = attn ( qk , qk , v , mask = mask ) # (2, 8, 2048, 64)

Jika Anda ingin modul secara otomatis memasukkan kueri/kunci/nilai serta mask Anda, cukup setel kata kunci autopad ke True

 import torch
from local_attention import LocalAttention

q = torch . randn ( 8 , 2057 , 64 )
k = torch . randn ( 8 , 2057 , 64 )
v = torch . randn ( 8 , 2057 , 64 )

attn = LocalAttention (
    window_size = 512 ,
    causal = True ,
    autopad = True      # auto pads both inputs and mask, then truncates output appropriately
)

mask = torch . ones ( 1 , 2057 ). bool ()
out = attn ( q , k , v , mask = mask ) # (8, 2057, 64)

Transformator Perhatian Lokal

Transformator perhatian lokal penuh

 import torch
from local_attention import LocalTransformer

model = LocalTransformer (
    num_tokens = 256 ,
    dim = 512 ,
    depth = 6 ,
    max_seq_len = 8192 ,
    causal = True ,
    local_attn_window_size = 256
). cuda ()

x = torch . randint ( 0 , 256 , ( 1 , 8192 )). cuda ()

logits = model ( x ) # (1, 8192, 256)

Enwik8 di 4096

ukuran jendela 256, lihat balik 1, total bidang reseptif 512

$ python train.py

Kutipan

 @inproceedings { rae-razavi-2020-transformers ,
    title   = " Do Transformers Need Deep Long-Range Memory? " ,
    author  = " Rae, Jack  and Razavi, Ali " ,
    booktitle = " Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics " ,
    month   = jul,
    year    = " 2020 " ,
    address = " Online " ,
    publisher = " Association for Computational Linguistics " ,
    url     = " https://www.aclweb.org/anthology/2020.acl-main.672 "
}

 @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 { beltagy2020longformer ,
    title   = { Longformer: The Long-Document Transformer } ,
    author  = { Iz Beltagy and Matthew E. Peters and Arman Cohan } ,
    year    = { 2020 } ,
    eprint  = { 2004.05150 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CL }
}

 @inproceedings { Sun2022ALT ,
    title     = { A Length-Extrapolatable Transformer } ,
    author    = { Yutao Sun and Li Dong and Barun Patra and Shuming Ma and Shaohan Huang and Alon Benhaim and Vishrav Chaudhary and Xia Song and Furu Wei } ,
    year      = { 2022 }
}

 @article { Bondarenko2023QuantizableTR ,
    title   = { Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing } ,
    author  = { Yelysei Bondarenko and Markus Nagel and Tijmen Blankevoort } ,
    journal = { ArXiv } ,
    year    = { 2023 } ,
    volume  = { abs/2306.12929 } ,
    url     = { https://api.semanticscholar.org/CorpusID:259224568 }
}