recurrent memory transformer pytorch
0.7.0
تنفيذ محول الذاكرة المتكررة (مراجعة مفتوحة) في Pytorch. لقد كان لديهم مؤخرًا ورقة متابعة قصيرة أظهرت أنهم قادرون على نسخ المعلومات عبر مليون رمز مميز على الأقل.
ليس هناك شك في ذهني أن RMT من شأنه أن يجعل وكيل RL أقوى من AdA، وهو مجرد محول XL - التحديث: محول الإجراء المتكرر مع الذاكرة (RATE)
مراجعة ورقة يانيك كيلشر
$ 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 ...مع ذكريات 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 ...تدريب على تسلسل طويل سخيف
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 قم بتحريك الدعامة الخلفية لإعادة تشغيل الذاكرة إلى وظيفة الشعلة، واختبر ثنائي الاتجاه، ثم اختبر مشكلة حقيقية
اجعل التضمينات الدوارة تعمل بشكل صحيح مع ذكريات XL
إضافة ذكريات XL، منفصلة
تقديم طريقة لإيقاف التضمينات الدوارة والتضمينات الموضعية المطلقة وإضافة إزاحة رمزية
جعل الذكريات مقنعة سببيا خيارا
إضافة تقنية backprop لإعادة تشغيل الذاكرة من ورق memformer
الترميز الموضعي النسبي
كتلة المحولات المتكررة
ميمفورمر
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} @misc { bulatov2023scaling ,
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} @inproceedings { dao2022flashattention ,
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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 } ,
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version = { v0.1-alpha } ,
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note = { under review }
} @misc { ding2021erniedoc ,
title = { ERNIE-Doc: A Retrospective Long-Document Modeling Transformer } ,
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} @article { Zhu2024HyperConnections ,
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} @inproceedings { Zhou2024ValueRL ,
title = { Value Residual Learning For Alleviating Attention Concentration In Transformers } ,
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}
