RETRO pytorch
0.3.9
在 Pytorch 中實作 RETRO（Deepmind 基於檢索的注意力網絡）。這會稍微偏離論文，使用旋轉嵌入進行相對位置編碼，以及 Faiss 庫而不是 Scann。
該庫利用 autofaiss 建立索引併計算所有區塊的 k 最近鄰。
Jay Alammar 解釋性博文
這種檢索器方法的賣點是在參數減少 10 倍的情況下達到 GPT-3 性能。在這個領域絕對值得進行更多的研究。
如果 DeepNet 論文的說法可信的話，我還包含了將檢索轉換器擴展到 1000 層所需的功能。
更新：Reddit 上有人授予我金獎。不知道是什麼，不過還是謝謝！
更新：Deepnorm 已在清華大學的 130B 模型中得到大規模驗證。現在建議您將use_deepnet設定為True進行訓練
$ pip install retro-pytorch import torch
from retro_pytorch import RETRO
retro = RETRO (
chunk_size = 64 , # the chunk size that is indexed and retrieved (needed for proper relative positions as well as causal chunked cross attention)
max_seq_len = 2048 , # max sequence length
enc_dim = 896 , # encoder model dim
enc_depth = 2 , # encoder depth
dec_dim = 796 , # decoder model dim
dec_depth = 12 , # decoder depth
dec_cross_attn_layers = ( 3 , 6 , 9 , 12 ), # decoder cross attention layers (with causal chunk cross attention)
heads = 8 , # attention heads
dim_head = 64 , # dimension per head
dec_attn_dropout = 0.25 , # decoder attention dropout
dec_ff_dropout = 0.25 , # decoder feedforward dropout
use_deepnet = True # turn on post-normalization with DeepNet residual scaling and initialization, for scaling to 1000 layers
)
seq = torch . randint ( 0 , 20000 , ( 2 , 2048 + 1 )) # plus one since it is split into input and labels for training
retrieved = torch . randint ( 0 , 20000 , ( 2 , 32 , 2 , 128 )) # retrieved tokens - (batch, num chunks, num retrieved neighbors, retrieved chunk with continuation)
loss = retro ( seq , retrieved , return_loss = True )
loss . backward ()
# do above for many steps TrainingWrapper的目的是將文字文件資料夾處理為必要的記憶體映射 numpy 數組以開始訓練RETRO 。
import torch
from retro_pytorch import RETRO , TrainingWrapper
# instantiate RETRO, fit it into the TrainingWrapper with correct settings
retro = RETRO (
max_seq_len = 2048 , # max sequence length
enc_dim = 896 , # encoder model dimension
enc_depth = 3 , # encoder depth
dec_dim = 768 , # decoder model dimensions
dec_depth = 12 , # decoder depth
dec_cross_attn_layers = ( 1 , 3 , 6 , 9 ), # decoder cross attention layers (with causal chunk cross attention)
heads = 8 , # attention heads
dim_head = 64 , # dimension per head
dec_attn_dropout = 0.25 , # decoder attention dropout
dec_ff_dropout = 0.25 # decoder feedforward dropout
). cuda ()
wrapper = TrainingWrapper (
retro = retro , # path to retro instance
knn = 2 , # knn (2 in paper was sufficient)
chunk_size = 64 , # chunk size (64 in paper)
documents_path = './text_folder' , # path to folder of text
glob = '**/*.txt' , # text glob
chunks_memmap_path = './train.chunks.dat' , # path to chunks
seqs_memmap_path = './train.seq.dat' , # path to sequence data
doc_ids_memmap_path = './train.doc_ids.dat' , # path to document ids per chunk (used for filtering neighbors belonging to same document)
max_chunks = 1_000_000 , # maximum cap to chunks
max_seqs = 100_000 , # maximum seqs
knn_extra_neighbors = 100 , # num extra neighbors to fetch
max_index_memory_usage = '100m' ,
current_memory_available = '1G'
)
# get the dataloader and optimizer (AdamW with all the correct settings)
train_dl = iter ( wrapper . get_dataloader ( batch_size = 2 , shuffle = True ))
optim = wrapper . get_optimizer ( lr = 3e-4 , wd = 0.01 )
# now do your training
# ex. one gradient step
seq , retrieved = map ( lambda t : t . cuda (), next ( train_dl ))
# seq - (2, 2049) - 1 extra token since split by seq[:, :-1], seq[:, 1:]
# retrieved - (2, 32, 2, 128) - 128 since chunk + continuation, each 64 tokens
loss = retro (
seq ,
retrieved ,
return_loss = True
)
# one gradient step
loss . backward ()
optim . step ()
optim . zero_grad ()
# do above for many steps, then ...
# topk sampling with retrieval at chunk boundaries
sampled = wrapper . generate ( filter_thres = 0.9 , temperature = 1.0 ) # (1, <2049) terminates early if all <eos>
# or you can generate with a prompt, knn retrieval for initial chunks all taken care of
prompt = torch . randint ( 0 , 1000 , ( 1 , 128 )) # start with two chunks worth of sequence
sampled = wrapper . generate ( prompt , filter_thres = 0.9 , temperature = 1.0 ) # (1, <2049) terminates early if all <eos>如果您希望強制重新處理訓練數據，只需使用REPROCESS=1環境標誌執行腳本，如下所示
$ REPROCESS=1 python train.pyRETRODataset類別接受多個記憶體映射的 numpy 數組的路徑，其中包含區塊、要訓練的序列中第一個區塊的索引（在 RETRO 解碼器中）以及每個區塊的 k 最近鄰居的預先計算的索引。
如果您不想使用上面的TrainingWrapper ，您可以使用它輕鬆地組裝RETRO訓練的資料。
此外，建立必要的記憶體映射資料所需的所有函數都在接下來的部分中。
import torch
from torch . utils . data import DataLoader
from retro_pytorch import RETRO , RETRODataset
# mock data constants
import numpy as np
NUM_CHUNKS = 1000
CHUNK_SIZE = 64
NUM_SEQS = 100
NUM_NEIGHBORS = 2
def save_memmap ( path , tensor ):
f = np . memmap ( path , dtype = tensor . dtype , mode = 'w+' , shape = tensor . shape )
f [:] = tensor
del f
# generate mock chunk data
save_memmap (
'./train.chunks.dat' ,
np . int32 ( np . random . randint ( 0 , 8192 , size = ( NUM_CHUNKS , CHUNK_SIZE + 1 )))
)
# generate nearest neighbors for each chunk
save_memmap (
'./train.chunks.knn.dat' ,
np . int32 ( np . random . randint ( 0 , 1000 , size = ( NUM_CHUNKS , NUM_NEIGHBORS )))
)
# generate seq data
save_memmap (
'./train.seq.dat' ,
np . int32 ( np . random . randint ( 0 , 128 , size = ( NUM_SEQS ,)))
)
# instantiate dataset class
# which constructs the sequence and neighbors from memmapped chunk and neighbor information
train_ds = RETRODataset (
num_sequences = NUM_SEQS ,
num_chunks = NUM_CHUNKS ,
num_neighbors = NUM_NEIGHBORS ,
chunk_size = CHUNK_SIZE ,
seq_len = 2048 ,
chunk_memmap_path = './train.chunks.dat' ,
chunk_nn_memmap_path = './train.chunks.knn.dat' ,
seq_memmap_path = './train.seq.dat'
)
train_dl = iter ( DataLoader ( train_ds , batch_size = 2 ))
# one forwards and backwards
retro = RETRO (
max_seq_len = 2048 , # max sequence length
enc_dim = 896 , # encoder model dimension
enc_depth = 3 , # encoder depth
dec_dim = 768 , # decoder model dimensions
dec_depth = 12 , # decoder depth
dec_cross_attn_layers = ( 1 , 3 , 6 , 9 ), # decoder cross attention layers (with causal chunk cross attention)
heads = 8 , # attention heads
dim_head = 64 , # dimension per head
dec_attn_dropout = 0.25 , # decoder attention dropout
dec_ff_dropout = 0.25 # decoder feedforward dropout
). cuda ()
seq , retrieved = map ( lambda t : t . cuda (), next ( train_dl ))
# seq - (2, 2049) - 1 extra token since split by seq[:, :-1], seq[:, 1:]
# retrieved - (2, 32, 2, 128) - 128 since chunk + continuation, each 64 tokens
loss = retro (
seq ,
retrieved ,
return_loss = True
)
loss . backward ()該存儲庫將使用 BERT 版本的預設分詞器（句子）。嵌入將從普通 BERT 中獲取，並且可以是屏蔽平均池化表示，也可以是 CLS 令牌。
前任。掩蔽平均合併表示
from retro_pytorch . retrieval import bert_embed , tokenize
ids = tokenize ([
'hello world' ,
'foo bar'
])
embeds = bert_embed ( ids ) # (2, 768) - 768 is hidden dimension of BERT前任。 CLS 代幣表示
from retro_pytorch . retrieval import bert_embed , tokenize
ids = tokenize ([
'hello world' ,
'foo bar'
])
embeds = bert_embed ( ids , return_cls_repr = True ) # (2, 768)使用text_folder_to_chunks_建立區塊和區塊起始索引（用於計算自迴歸訓練的序列範圍）
from retro_pytorch . retrieval import text_folder_to_chunks_
stats = text_folder_to_chunks_ (
folder = './text_folder' ,
glob = '**/*.txt' ,
chunks_memmap_path = './train.chunks.dat' ,
seqs_memmap_path = './train.seq.dat' ,
doc_ids_memmap_path = './train.doc_ids.dat' , # document ids are needed for filtering out neighbors belonging to same document appropriately during computation of nearest neighbors
chunk_size = 64 ,
seq_len = 2048 ,
max_chunks = 1_000_000 ,
max_seqs = 100_000
)
# {'chunks': <number of chunks>, 'docs': <number of documents>, 'seqs': <number of sequences>} 您可以使用一個命令將記憶體映射塊 numpy 數組轉換為嵌入和 faiss 索引
from retro_pytorch . retrieval import chunks_to_index_and_embed
index , embeddings = chunks_to_index_and_embed (
num_chunks = 1000 ,
chunk_size = 64 ,
chunk_memmap_path = './train.chunks.dat'
)
query_vector = embeddings [: 1 ] # use first embedding as query
_ , indices = index . search ( query_vector , k = 2 ) # fetch 2 neighbors, first indices should be self
neighbor_embeddings = embeddings [ indices ] # (1, 2, 768)您也可以使用chunks_to_precalculated_knn_指令直接計算訓練所需的最近鄰文件
from retro_pytorch . retrieval import chunks_to_precalculated_knn_
chunks_to_precalculated_knn_ (
num_chunks = 1000 ,
chunk_size = 64 ,
chunk_memmap_path = './train.chunks.dat' , # path to main chunks dataset
doc_ids_memmap_path = './train.doc_ids.dat' , # path to document ids created by text_folder_to_chunks_, used for filtering out neighbors that belong to the same document
num_nearest_neighbors = 2 , # number of nearest neighbors you'd like to use
num_extra_neighbors = 10 # fetch 10 extra neighbors, in the case that fetched neighbors are frequently from same document (filtered out)
)
# nearest neighbor info saved to ./train.chunks.knn.dat @misc { borgeaud2022improving ,
title = { Improving language models by retrieving from trillions of tokens } ,
author = { Sebastian Borgeaud and Arthur Mensch and Jordan Hoffmann and Trevor Cai and Eliza Rutherford and Katie Millican and George van den Driessche and Jean-Baptiste Lespiau and Bogdan Damoc and Aidan Clark and Diego de Las Casas and Aurelia Guy and Jacob Menick and Roman Ring and Tom Hennigan and Saffron Huang and Loren Maggiore and Chris Jones and Albin Cassirer and Andy Brock and Michela Paganini and Geoffrey Irving and Oriol Vinyals and Simon Osindero and Karen Simonyan and Jack W. Rae and Erich Elsen and Laurent Sifre } ,
year = { 2022 } ,
eprint = { 2112.04426 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL }
} @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 }
} @article { Wang2022DeepNetST ,
title = { DeepNet: Scaling Transformers to 1, 000 Layers } ,
author = { Hongyu Wang and Shuming Ma and Li Dong and Shaohan Huang and Dongdong Zhang and Furu Wei } ,
journal = { ArXiv } ,
year = { 2022 } ,
volume = { abs/2203.00555 }
} @misc { zhang2021sparse ,
title = { Sparse Attention with Linear Units } ,
author = { Biao Zhang and Ivan Titov and Rico Sennrich } ,
year = { 2021 } ,
eprint = { 2104.07012 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL }
}我始終認為成年生活就是童年的不斷恢復。 ——翁貝托‧艾柯
