recurrent memory transformer pytorch
0.7.0
Implementación de Transformador de memoria recurrente (revisión abierta) en Pytorch. Recientemente tuvieron un breve artículo de seguimiento que demostró que era capaz de copiar información en al menos 1 millón de tokens.
No tengo ninguna duda de que RMT sería un agente de RL más fuerte que AdA, que es solo un Transformer-XL - Actualización: Transformador de acción recurrente con memoria (RATE)
Revisión del artículo de Yannic Kilcher
$ pip install recurrent-memory-transformer-pytorch import torch
from recurrent_memory_transformer_pytorch import RecurrentMemoryTransformer
model = RecurrentMemoryTransformer (
num_tokens = 20000 , # number of tokens
num_memory_tokens = 128 , # number of memory tokens, this will determine the bottleneck for information being passed to the future
dim = 512 , # model dimensions
depth = 6 , # transformer depth
causal = True , # autoregressive or not
dim_head = 64 , # dimension per head
heads = 8 , # heads
seq_len = 1024 , # sequence length of a segment
use_flash_attn = True # whether to use flash attention
)
x = torch . randint ( 0 , 256 , ( 1 , 1024 ))
logits1 , mem1 , _ = model ( x ) # (1, 1024, 20000), (1, 128, 512), None
logits2 , mem2 , _ = model ( x , mem1 ) # (1, 1024, 20000), (1, 128, 512), None
logits3 , mem3 , _ = model ( x , mem2 ) # (1, 1024, 20000), (1, 128, 512), None
# and so on ...Con memorias XL
import torch
from recurrent_memory_transformer_pytorch import RecurrentMemoryTransformer
model = RecurrentMemoryTransformer (
num_tokens = 20000 ,
num_memory_tokens = 128 ,
dim = 512 ,
depth = 6 ,
causal = True ,
dim_head = 64 ,
heads = 8 ,
seq_len = 1024 ,
use_flash_attn = True ,
use_xl_memories = True , # set this to True
xl_mem_len = 512 # can be shorter than the seq len - i think just having a bit of the past will prevent much of the RMT memories memorizing the immediate preceding text
)
x = torch . randint ( 0 , 256 , ( 1 , 1024 ))
logits1 , mem1 , xl_mem1 = model ( x ) # (1, 1024, 20000), (1, 128, 512), [(2, 1, 512, 512)]
logits2 , mem2 , xl_mem2 = model ( x , mem1 , xl_memories = xl_mem1 ) # (1, 1024, 20000), (1, 128, 512), [(2, 1, 512, 512)]
logits3 , mem3 , xl_mem3 = model ( x , mem2 , xl_memories = xl_mem2 ) # (1, 1024, 20000), (1, 128, 512), [(2, 1, 512, 512)]
# and so on ...Entrena en una secuencia absurdamente larga
import torch
from recurrent_memory_transformer_pytorch import (
RecurrentMemoryTransformer ,
RecurrentMemoryTransformerWrapper
)
model = RecurrentMemoryTransformer (
num_tokens = 256 ,
num_memory_tokens = 128 ,
dim = 512 ,
depth = 6 ,
seq_len = 1024 ,
use_flash_attn = True ,
causal = True
)
model = RecurrentMemoryTransformerWrapper ( model ). cuda ()
seq = torch . randint ( 0 , 256 , ( 4 , 65536 )). cuda () # absurdly long sequence, in reality, they curriculum learned this starting with 1 segment to about 7-8 segments
loss = model ( seq , memory_replay_backprop = True ) # memory efficient training from memformer paper mueva la reproducción de memoria a una función de antorcha, pruebe bidireccional y luego pruebe en un problema real
conseguir que las incrustaciones rotativas funcionen correctamente con memorias xl
agregar memorias xl, separadas
Ofrecer una forma de desactivar las incrustaciones rotativas, las incrustaciones posicionales absolutas y agregar desplazamiento simbólico.
hacer que los recuerdos estén causalmente enmascarados como una opción
agregue la técnica de reproducción de memoria del papel memformer
codificación posicional relativa
Transformador recurrente de bloque
Formador de memoria
@inproceedings { bulatov2022recurrent ,
title = { Recurrent Memory Transformer } ,
author = { Aydar Bulatov and Yuri Kuratov and Mikhail Burtsev } ,
booktitle = { Advances in Neural Information Processing Systems } ,
editor = { Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho } ,
year = { 2022 } ,
url = { https://openreview.net/forum?id=Uynr3iPhksa }
} @misc { bulatov2023scaling ,
title = { Scaling Transformer to 1M tokens and beyond with RMT } ,
author = { Aydar Bulatov and Yuri Kuratov and Mikhail S. Burtsev } ,
year = { 2023 } ,
eprint = { 2304.11062 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL }
} @inproceedings { dao2022flashattention ,
title = { Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness } ,
author = { Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{'e}, Christopher } ,
booktitle = { Advances in Neural Information Processing Systems } ,
year = { 2022 }
} @misc { shazeer2020glu ,
title = { GLU Variants Improve Transformer } ,
author = { Noam Shazeer } ,
year = { 2020 } ,
url = { https://arxiv.org/abs/2002.05202 }
} @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 }
} @inproceedings { Wu2020MemformerAM ,
title = { Memformer: A Memory-Augmented Transformer for Sequence Modeling } ,
author = { Qingyang Wu and Zhenzhong Lan and Kun Qian and Jing Gu and Alborz Geramifard and Zhou Yu } ,
booktitle = { AACL/IJCNLP } ,
year = { 2020 }
} @software { peng_bo_2021_5196578 ,
author = { PENG Bo } ,
title = { BlinkDL/RWKV-LM: 0.01 } ,
month = { aug } ,
year = { 2021 } ,
publisher = { Zenodo } ,
version = { 0.01 } ,
doi = { 10.5281/zenodo.5196578 } ,
url = { https://doi.org/10.5281/zenodo.5196578 }
} @misc { ding2021cogview ,
title = { CogView: Mastering Text-to-Image Generation via Transformers } ,
author = { Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang } ,
year = { 2021 } ,
eprint = { 2105.13290 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CV }
} @software { Dayma_DALLE_Mini_2021 ,
author = { Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata } ,
doi = { 10.5281/zenodo.5146400 } ,
license = { Apache-2.0 } ,
month = { jul } ,
title = { {DALL·E Mini} } ,
url = { https://github.com/borisdayma/dalle-mini } ,
version = { v0.1-alpha } ,
year = { 2021 } } @inproceedings { anonymous2022normformer ,
title = { NormFormer: Improved Transformer Pretraining with Extra Normalization } ,
author = { Anonymous } ,
booktitle = { Submitted to The Tenth International Conference on Learning Representations } ,
year = { 2022 } ,
url = { https://openreview.net/forum?id=GMYWzWztDx5 } ,
note = { under review }
} @misc { ding2021erniedoc ,
title = { ERNIE-Doc: A Retrospective Long-Document Modeling Transformer } ,
author = { Siyu Ding and Junyuan Shang and Shuohuan Wang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang } ,
year = { 2021 } ,
eprint = { 2012.15688 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL }
} @article { Zhu2024HyperConnections ,
title = { Hyper-Connections } ,
author = { Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou } ,
journal = { ArXiv } ,
year = { 2024 } ,
volume = { abs/2409.19606 } ,
url = { https://api.semanticscholar.org/CorpusID:272987528 }
} @inproceedings { Zhou2024ValueRL ,
title = { Value Residual Learning For Alleviating Attention Concentration In Transformers } ,
author = { Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan } ,
year = { 2024 } ,
url = { https://api.semanticscholar.org/CorpusID:273532030 }
}
