routing transformer
1.6.1
Una implementación con todas las funciones de Routing Transformer. El artículo propone utilizar k-means para enrutar consultas/claves similares al mismo grupo para llamar la atención.
131k fichas
$ pip install routing_transformerUn modelo de lenguaje simple
import torch
from routing_transformer import RoutingTransformerLM
model = RoutingTransformerLM (
num_tokens = 20000 ,
dim = 512 ,
heads = 8 ,
depth = 12 ,
max_seq_len = 8192 ,
causal = True , # auto-regressive or not
emb_dim = 128 , # embedding factorization, from Albert
weight_tie = False , # weight tie layers, from Albert
tie_embedding = False , # multiply final embeddings with token weights for logits
dim_head = 64 , # be able to fix the dimension of each head, making it independent of the embedding dimension and the number of heads
attn_dropout = 0.1 , # dropout after attention
attn_layer_dropout = 0. , # dropout after self attention layer
ff_dropout = 0.1 , # feedforward dropout
layer_dropout = 0. , # layer dropout
window_size = 128 , # target window size of each cluster
n_local_attn_heads = 4 , # number of local attention heads
reversible = True , # reversible networks for memory savings, from Reformer paper
ff_chunks = 10 , # feed forward chunking, from Reformer paper
ff_glu = True , # use GLU variant in feedforward
pkm_layers = ( 4 , 7 ), # specify layers to use product key memory. paper shows 1 or 2 modules near the middle of the transformer is best
pkm_num_keys = 128 , # defaults to 128, but can be increased to 256 or 512 as memory allows
moe_layers = ( 3 , 6 ), # specify which layers to use mixture of experts
moe_num_experts = 4 , # number of experts in the mixture of experts layer, defaults to 4. increase for adding more parameters to model
moe_loss_coef = 1e-2 , # the weight for the auxiliary loss in mixture of experts to keep expert usage balanced
num_mem_kv = 8 , # number of memory key/values to append to each cluster of each head, from the 'All-Attention' paper. defaults to 1 in the causal case for unshared QK to work
use_scale_norm = False , # use scale norm, simplified normalization from 'Transformers without Tears' paper
use_rezero = False , # use Rezero with no normalization
shift_tokens = True # shift tokens by one along sequence dimension, for a slight improvement in convergence
). cuda ()
x = torch . randint ( 0 , 20000 , ( 1 , 8192 )). long (). cuda ()
input_mask = torch . ones_like ( x ). bool (). cuda ()
y , aux_loss = model ( x , input_mask = input_mask ) # (1, 8192, 20000)
aux_loss . backward () # add auxiliary loss to main loss before backpropUn transformador sencillo
import torch
from routing_transformer import RoutingTransformer
model = RoutingTransformer (
dim = 512 ,
heads = 8 ,
depth = 12 ,
max_seq_len = 8192 ,
window_size = 128 ,
n_local_attn_heads = 4
). cuda ()
x = torch . randn ( 1 , 8192 , 512 ). cuda ()
input_mask = torch . ones ( 1 , 8192 ). bool (). cuda ()
y , aux_loss = model ( x , input_mask = input_mask ) # (1, 8192, 512)
aux_loss . backward () # add auxiliary loss to main loss before backprop Para utilizar un codificador y decodificador completo, simplemente importe la clase RoutingTransformerEncDec . Exceptuando la palabra clave dim , todas las demás palabras clave se antepondrán enc_ o dec_ para la clase RoutingTransformerLM del codificador y decodificador, respectivamente.
import torch
from routing_transformer import RoutingTransformerEncDec
model = RoutingTransformerEncDec (
dim = 512 ,
enc_num_tokens = 20000 ,
enc_depth = 4 ,
enc_heads = 8 ,
enc_max_seq_len = 4096 ,
enc_window_size = 128 ,
dec_num_tokens = 20000 ,
dec_depth = 4 ,
dec_heads = 8 ,
dec_max_seq_len = 4096 ,
dec_window_size = 128 ,
dec_reversible = True
). cuda ()
src = torch . randint ( 0 , 20000 , ( 1 , 4096 )). cuda ()
tgt = torch . randint ( 0 , 20000 , ( 1 , 4096 )). cuda ()
src_mask = torch . ones_like ( src ). bool (). cuda ()
tgt_mask = torch . ones_like ( tgt ). bool (). cuda ()
loss , aux_loss = model ( src , tgt , enc_input_mask = src_mask , dec_input_mask = tgt_mask , return_loss = True , randomly_truncate_sequence = True )
loss . backward ()
aux_loss . backward ()
# do your training, then to sample up to 2048 tokens based on the source sequence
src = torch . randint ( 0 , 20000 , ( 1 , 4096 )). cuda ()
start_tokens = torch . ones ( 1 , 1 ). long (). cuda () # assume starting token is 1
sample = model . generate ( src , start_tokens , seq_len = 2048 , eos_token = 2 ) # (1, <= 2048, 20000) Para ver los beneficios de usar PKM, la tasa de aprendizaje de los valores debe establecerse más alta que el resto de los parámetros. (Se recomienda ser 1e-2 )
Puede seguir las instrucciones aquí para configurarlo correctamente https://github.com/lucidrains/product-key-memory#learning-rates
kmeans_ema_decay = {defaults to 0.999}Esta es la caída exponencial de la media móvil para actualizar las k-medias. Cuanto más bajo sea, más rápido se ajustarán los medios, pero a costa de la estabilidad.
commitment_factor = {defaults to 1e-4}El peso de la pérdida auxiliar que anima a los tokens a acercarse (comprometerse) a los centroides k-media que fueron elegidos para ellos.
Las siguientes instrucciones le permitirán actualizar los kmeans manualmente. De forma predeterminada, los kmeans se actualizan automáticamente en cada paso hacia atrás.
import torch
from routing_transformer import RoutingTransformerLM , AutoregressiveWrapper
model = RoutingTransformerLM (
num_tokens = 20000 ,
dim = 1024 ,
heads = 8 ,
depth = 6 ,
window_size = 256 ,
max_seq_len = 8192 ,
causal = True ,
_register_kmeans_update = False # set to False to disable auto-updating
)
model = AutoregressiveWrapper ( model )
x = torch . randint ( 0 , 20000 , ( 1 , 8192 ))
loss = model ( x , return_loss = True )
loss . backward ()
# update kmeans with this call
model . update_kmeans ()Esta arquitectura tiene problemas para generalizarse a longitudes de secuencia más cortas al decodificar tokens desde 1 -> longitud máxima de secuencia. La solución más sencilla y segura es truncar aleatoriamente la secuencia durante el entrenamiento. Esto ayuda a que la red y los kmeans se generalicen a un número variable de tokens, a costa de una formación prolongada.
Si está preparando la red con la longitud completa de la secuencia al inicio, no enfrentará este problema y podrá omitir este procedimiento de capacitación.
import torch
from routing_transformer import RoutingTransformerLM , AutoregressiveWrapper
model = RoutingTransformerLM (
num_tokens = 20000 ,
dim = 1024 ,
heads = 8 ,
depth = 12 ,
window_size = 256 ,
max_seq_len = 8192 ,
causal = True
)
model = AutoregressiveWrapper ( model )
x = torch . randint ( 0 , 20000 , ( 1 , 8192 ))
loss = model ( x , return_loss = True , randomly_truncate_sequence = True ) # (1, 8192, 20000) Un agradecimiento especial a Aran Komatsuzaki por iniciar la implementación inicial en Pytorch que evolucionó hasta convertirse en esta biblioteca.
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eprint = { 1907.05242 } ,
archivePrefix = { arXiv }
} @article { DBLP:journals/corr/abs-1907-01470 ,
author = { Sainbayar Sukhbaatar and
Edouard Grave and
Guillaume Lample and
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} @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 }
}
