recurrent memory transformer pytorch
0.7.0
การใช้งาน Recurrent Memory Transformer (openreview) ใน Pytorch พวกเขามีรายงานติดตามผลสั้นๆ เมื่อเร็ว ๆ นี้ ซึ่งแสดงให้เห็นว่าสามารถคัดลอกข้อมูลไปยังโทเค็น 1 ล้านเป็นอย่างน้อยที่สุด
ไม่ต้องสงสัยเลยว่า RMT จะสร้างตัวแทน RL ที่แข็งแกร่งกว่า AdA ซึ่งเป็นเพียง Transformer-XL - อัปเดต: Recurrent Action Transformer พร้อมหน่วยความจำ (RATE)
บทวิจารณ์บทความของ 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 ...พร้อมความทรงจำ 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 ...ฝึกฝนตามลำดับที่ยาวอย่างไร้เหตุผล
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 ย้าย backprop ที่เล่นซ้ำหน่วยความจำไปที่ torch.function ทดสอบแบบสองทิศทาง จากนั้นทดสอบกับปัญหาจริง
ทำให้การฝังแบบหมุนทำงานอย่างถูกต้องกับความทรงจำ xl
เพิ่มความทรงจำ xl แยกออก
เสนอวิธีการปิดการฝังแบบหมุน การฝังตำแหน่งแบบสัมบูรณ์ และเพิ่มการเปลี่ยนโทเค็น
ทำให้ความทรงจำถูกปกปิดไว้เป็นทางเลือก
เพิ่มเทคนิค backprop การเล่นซ้ำหน่วยความจำจากกระดาษ memformer
การเข้ารหัสตำแหน่งสัมพัทธ์
บล็อกหม้อแปลงกระแสซ้ำ
เมมฟอร์เมอร์
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