整体的目录如下所示： 上述的项目结构中出了model是必须的外，其他的都可以根据训练的代码参数传入进行调整，有些不需要一定存在 data
train.pkl:对原始训练语料进行tokenize之后的文件,存储一个list对象，list的每条数据表示一个多轮对话，表示一条训练数据 model:存放对话生成的模型 vocab
vocab.txt:字典文件。默认的字典大小为13317，若需要使用自定义字典，需要将confog.json文件中的vocab_size字段设为相应的大小。 sample:存放人机闲聊生成的历史聊天记录 train.py:训练代码 interact.py:人机交互代码 preprocess.py:数据预处理代码 第一步：数据模块， 根据后面的数据集地址介绍进行数据集的下载，里面有各个地方的数据集来源以及数据合并的代码 第二步：将得到的数据通过preprocess.py文件进行训练数据处理，得到train.pkl文件，得到后再整个项目目录下创建data文件夹，并将得到的train.pkl文件移动到data文件夹下面 第三步：去huggingface网站上面下载gpt2预训练模型下的文件，具体需要下载的文件如下所示： 第四步：运行train.py文件训练得到再收集的数据集上面的微调模型 第五步：通过chatbot.py对模型进行人机交互和推理 https://github.com/codemayq/chinese-chatbot-corpus， 安装上面的readme运行就可以得到相应的数据集 然后再运行上面的preprocess.py就可以得到相关的训练数据集train.pkl
import argparse
import math
import time
import torch
import torch. nn. functional as F
import torch. optim as optim
import logging
from datetime import datetime
import os
from torch. utils. data import Dataset, DataLoader
from os. path import join, exists
from torch. nn import CrossEntropyLoss
from tqdm import tqdm
from torch. nn import DataParallel
import transformers
import pickle
import sys
from pytorchtools import EarlyStopping
from sklearn. model_selection import train_test_split
from data_parallel import BalancedDataParallel
from transformers import GPT2TokenizerFast, GPT2LMHeadModel, GPT2Config
from transformers import BertTokenizerFast
import pandas as pd
import torch. nn. utils. rnn as rnn_utils
import numpy as np
from dataset import MyDataset
def set_args ( ) :
parser = argparse. ArgumentParser( )
parser. add_argument( '--device' , default= '3' , type = str , required= False , help = '设置使用哪些显卡' )
parser. add_argument( '--no_cuda' , action= 'store_true' , help = '不使用GPU进行训练' )
parser. add_argument( '--vocab_path' , default= 'vocab/vocab.txt' , type = str , required= False ,
help = '词表路径' )
parser. add_argument( '--model_config' , default= 'config/config.json' , type = str , required= False ,
help = '设置模型参数' )
parser. add_argument( '--train_path' , default= 'data/train.pkl' , type = str , required= False , help = '训练集路径' )
parser. add_argument( '--max_len' , default= 150 , type = int , required= False , help = '训练时，输入数据的最大长度' )
parser. add_argument( '--log_path' , default= 'data/train.log' , type = str , required= False , help = '训练日志存放位置' )
parser. add_argument( '--log' , default= True , help = "是否记录日志" )
parser. add_argument( '--ignore_index' , default= - 100 , type = int , required= False , help = '对于ignore_index的label token不计算梯度' )
parser. add_argument( '--epochs' , default= 20 , type = int , required= False , help = '训练的最大轮次' )
parser. add_argument( '--batch_size' , default= 64 , type = int , required= False , help = '训练的batch size' )
parser. add_argument( '--gpu0_bsz' , default= 10 , type = int , required= False , help = '0号卡的batch size' )
parser. add_argument( '--lr' , default= 2.6e-5 , type = float , required= False , help = '学习率' )
parser. add_argument( '--eps' , default= 1.0e-09 , type = float , required= False , help = '衰减率' )
parser. add_argument( '--log_step' , default= 1 , type = int , required= False , help = '多少步汇报一次loss' )
parser. add_argument( '--gradient_accumulation_steps' , default= 4 , type = int , required= False , help = '梯度积累' )
parser. add_argument( '--max_grad_norm' , default= 2.0 , type = float , required= False )
parser. add_argument( '--save_model_path' , default= 'model_new' , type = str , required= False ,
help = '模型输出路径' )
parser. add_argument( '--pretrained_model' , default= './pretrained_model' , type = str , required= False ,
help = '预训练的模型的路径' )
parser. add_argument( '--num_workers' , type = int , default= 0 , help = "dataloader加载数据时使用的线程数量" )
parser. add_argument( '--patience' , type = int , default= 0 , help = "用于early stopping,设为0时,不进行early stopping.early stop得到的模型的生成效果不一定会更好。" )
parser. add_argument( '--warmup_steps' , type = int , default= 4000 , help = 'warm up步数' )
parser. add_argument( '--val_num' , type = int , default= 8000 , help = '验证集大小' )
args = parser. parse_args( )
return args
def create_logger ( args) :
"""
将日志输出到日志文件和控制台
"""
logger = logging. getLogger( __name__)
logger. setLevel( logging. INFO)
formatter = logging. Formatter(
'%(asctime)s - %(levelname)s - %(message)s' )
file_handler = logging. FileHandler(
filename= args. log_path)
file_handler. setFormatter( formatter)
file_handler. setLevel( logging. INFO)
logger. addHandler( file_handler)
console = logging. StreamHandler( )
console. setLevel( logging. DEBUG)
console. setFormatter( formatter)
logger. addHandler( console)
return logger
def collate_fn ( batch) :
input_ids = rnn_utils. pad_sequence( batch, batch_first= True , padding_value= 0 )
labels = rnn_utils. pad_sequence( batch, batch_first= True , padding_value= - 100 )
return input_ids, labels
def load_dataset ( logger, args) :
"""
加载训练集和验证集
"""
logger. info( "loading training dataset and validating dataset" )
train_path = args. train_path
with open ( train_path, "rb" ) as f:
input_list = pickle. load( f)
val_num = args. val_num
input_list_train = input_list[ val_num: ]
input_list_val = input_list[ : val_num]
train_dataset = MyDataset( input_list_train, args. max_len)
val_dataset = MyDataset( input_list_val, args. max_len)
return train_dataset, val_dataset
def train_epoch ( model, train_dataloader, optimizer, scheduler, logger,
epoch, args) :
model. train( )
device = args. device
ignore_index = args. ignore_index
epoch_start_time = datetime. now( )
total_loss = 0
epoch_correct_num, epoch_total_num = 0 , 0
for batch_idx, ( input_ids, labels) in enumerate ( train_dataloader) :
try :
input_ids = input_ids. to( device)
labels = labels. to( device)
outputs = model. forward( input_ids, labels= labels)
logits = outputs. logits
loss = outputs. loss
loss = loss. mean( )
batch_correct_num, batch_total_num = calculate_acc( logits, labels, ignore_index= ignore_index)
epoch_correct_num += batch_correct_num
epoch_total_num += batch_total_num
batch_acc = batch_correct_num / batch_total_num
total_loss += loss. item( )
if args. gradient_accumulation_steps > 1 :
loss = loss / args. gradient_accumulation_steps
loss. backward( )
torch. nn. utils. clip_grad_norm_( model. parameters( ) , args. max_grad_norm)
if ( batch_idx + 1 ) % args. gradient_accumulation_steps == 0 :
optimizer. step( )
scheduler. step( )
optimizer. zero_grad( )
if ( batch_idx + 1 ) % args. log_step == 0 :
logger. info(
"batch {} of epoch {}, loss {}, batch_acc {}, lr {}" . format (
batch_idx + 1 , epoch + 1 , loss. item( ) * args. gradient_accumulation_steps, batch_acc, scheduler. get_lr( ) ) )
del input_ids, outputs
except RuntimeError as exception:
if "out of memory" in str ( exception) :
logger. info( "WARNING: ran out of memory" )
if hasattr ( torch. cuda, 'empty_cache' ) :
torch. cuda. empty_cache( )
else :
logger. info( str ( exception) )
raise exception
epoch_mean_loss = total_loss / len ( train_dataloader)
epoch_mean_acc = epoch_correct_num / epoch_total_num
logger. info(
"epoch {}: loss {}, predict_acc {}" . format ( epoch + 1 , epoch_mean_loss, epoch_mean_acc) )
logger. info( 'saving model for epoch {}' . format ( epoch + 1 ) )
model_path = join( args. save_model_path, 'epoch{}' . format ( epoch + 1 ) )
if not os. path. exists( model_path) :
os. mkdir( model_path)
model_to_save = model. module if hasattr ( model, 'module' ) else model
model_to_save. save_pretrained( model_path)
logger. info( 'epoch {} finished' . format ( epoch + 1 ) )
epoch_finish_time = datetime. now( )
logger. info( 'time for one epoch: {}' . format ( epoch_finish_time - epoch_start_time) )
return epoch_mean_loss
def validate_epoch ( model, validate_dataloader, logger, epoch, args) :
logger. info( "start validating" )
model. eval ( )
device = args. device
ignore_index = args. ignore_index
epoch_start_time = datetime. now( )
total_loss = 0
try :
with torch. no_grad( ) :
for batch_idx, ( input_ids, labels) in enumerate ( validate_dataloader) :
input_ids = input_ids. to( device)
labels = labels. to( device)
outputs = model. forward( input_ids, labels= labels)
logits = outputs. logits
loss = outputs. loss
loss = loss. mean( )
total_loss += loss. item( )
del input_ids, outputs
epoch_mean_loss = total_loss / len ( validate_dataloader)
logger. info(
"validate epoch {}: loss {}" . format ( epoch+ 1 , epoch_mean_loss) )
epoch_finish_time = datetime. now( )
logger. info( 'time for validating one epoch: {}' . format ( epoch_finish_time - epoch_start_time) )
return epoch_mean_loss
except RuntimeError as exception:
if "out of memory" in str ( exception) :
logger. info( "WARNING: ran out of memory" )
if hasattr ( torch. cuda, 'empty_cache' ) :
torch. cuda. empty_cache( )
else :
logger. info( str ( exception) )
raise exception
def train ( model, logger, train_dataset, validate_dataset, args) :
train_dataloader = DataLoader(
train_dataset, batch_size= args. batch_size, shuffle= True , num_workers= args. num_workers, collate_fn= collate_fn,
drop_last= True
)
validate_dataloader = DataLoader( validate_dataset, batch_size= args. batch_size, shuffle= True ,
num_workers= args. num_workers, collate_fn= collate_fn, drop_last= True )
early_stopping = EarlyStopping( args. patience, verbose= True , save_path= args. save_model_path)
t_total = len ( train_dataloader) // args. gradient_accumulation_steps * args. epochs
optimizer = transformers. AdamW( model. parameters( ) , lr= args. lr, eps= args. eps)
scheduler = transformers. get_linear_schedule_with_warmup(
optimizer, num_warmup_steps= args. warmup_steps, num_training_steps= t_total
)
logger. info( 'starting training' )
train_losses, validate_losses = [ ] , [ ]
best_val_loss = 10000
for epoch in range ( args. epochs) :
train_loss = train_epoch(
model= model, train_dataloader= train_dataloader,
optimizer= optimizer, scheduler= scheduler,
logger= logger, epoch= epoch, args= args)
train_losses. append( train_loss)
validate_loss = validate_epoch(
model= model, validate_dataloader= validate_dataloader,
logger= logger, epoch= epoch, args= args)
validate_losses. append( validate_loss)
if validate_loss < best_val_loss:
best_val_loss = validate_loss
logger. info( 'saving current best model for epoch {}' . format ( epoch + 1 ) )
model_path = join( args. save_model_path, 'min_ppl_model' . format ( epoch + 1 ) )
if not os. path. exists( model_path) :
os. mkdir( model_path)
model_to_save = model. module if hasattr ( model, 'module' ) else model
model_to_save. save_pretrained( model_path)
if args. patience == 0 :
continue
early_stopping( validate_loss, model)
if early_stopping. early_stop:
logger. info( "Early stopping" )
break
logger. info( 'training finished' )
logger. info( "train_losses:{}" . format ( train_losses) )
logger. info( "validate_losses:{}" . format ( validate_losses) )
def caculate_loss ( logit, target, pad_idx, smoothing= True ) :
if smoothing:
logit = logit[ . . . , : - 1 , : ] . contiguous( ) . view( - 1 , logit. size( 2 ) )
target = target[ . . . , 1 : ] . contiguous( ) . view( - 1 )
eps = 0.1
n_class = logit. size( - 1 )
one_hot = torch. zeros_like( logit) . scatter( 1 , target. view( - 1 , 1 ) , 1 )
one_hot = one_hot * ( 1 - eps) + ( 1 - one_hot) * eps / ( n_class - 1 )
log_prb = F. log_softmax( logit, dim= 1 )
non_pad_mask = target. ne( pad_idx)
loss = - ( one_hot * log_prb) . sum ( dim= 1 )
loss = loss. masked_select( non_pad_mask) . mean( )
else :
logit = logit[ . . . , : - 1 , : ] . contiguous( ) . view( - 1 , logit. size( - 1 ) )
labels = target[ . . . , 1 : ] . contiguous( ) . view( - 1 )
loss = F. cross_entropy( logit, labels, ignore_index= pad_idx)
return loss
def calculate_acc ( logit, labels, ignore_index= - 100 ) :
logit = logit[ . . . , : - 1 , : ] . contiguous( ) . view( - 1 , logit. size( - 1 ) )
labels = labels[ . . . , 1 : ] . contiguous( ) . view( - 1 )
_, logit = logit. max ( dim= - 1 )
non_pad_mask = labels. ne( ignore_index)
n_correct = logit. eq( labels) . masked_select( non_pad_mask) . sum ( ) . item( )
n_word = non_pad_mask. sum ( ) . item( )
return n_correct, n_word
def main ( ) :
args = set_args( )
os. environ[ "CUDA_VISIBLE_DEVICES" ] = args. device
args. cuda = not args. no_cuda
if args. batch_size < 2048 and args. warmup_steps <= 4000 :
print ( '[Warning] The warmup steps may be not enough.\n' \
'(sz_b, warmup) = (2048, 4000) is the official setting.\n' \
'Using smaller batch w/o longer warmup may cause ' \
'the warmup stage ends with only little data trained.' )
logger = create_logger( args)
args. cuda = torch. cuda. is_available( ) and not args. no_cuda
device = 'cuda:0' if args. cuda else 'cpu'
args. device = device
logger. info( 'using device:{}' . format ( device) )
tokenizer = BertTokenizerFast( vocab_file= args. vocab_path, sep_token= "[SEP]" , pad_token= "[PAD]" , cls_token= "[CLS]" )
args. sep_id = tokenizer. sep_token_id
args. pad_id = tokenizer. pad_token_id
args. cls_id = tokenizer. cls_token_id
if not os. path. exists( args. save_model_path) :
os. mkdir( args. save_model_path)
if args. pretrained_model:
model = GPT2LMHeadModel. from_pretrained( args. pretrained_model)
else :
model_config = GPT2Config. from_json_file( args. model_config)
model = GPT2LMHeadModel( config= model_config)
model = model. to( device)
logger. info( 'model config:\n{}' . format ( model. config. to_json_string( ) ) )
assert model. config. vocab_size == tokenizer. vocab_size
if args. cuda and torch. cuda. device_count( ) > 1 :
model = DataParallel( model) . cuda( )
logger. info( "use GPU {} to train" . format ( args. device) )
num_parameters = 0
parameters = model. parameters( )
for parameter in parameters:
num_parameters += parameter. numel( )
logger. info( 'number of model parameters: {}' . format ( num_parameters) )
logger. info( "args:{}" . format ( args) )
train_dataset, validate_dataset = load_dataset( logger, args)
train( model, logger, train_dataset, validate_dataset, args)
if __name__ == '__main__' :
main( )
from torch. utils. data import Dataset
import torch
class MyDataset ( Dataset) :
"""
"""
def __init__ ( self, input_list, max_len) :
self. input_list = input_list
self. max_len = max_len
def __getitem__ ( self, index) :
input_ids = self. input_list[ index]
input_ids = input_ids[ : self. max_len]
input_ids = torch. tensor( input_ids, dtype= torch. long )
return input_ids
def __len__ ( self) :
return len ( self. input_list)
from tokenizers import BertWordPieceTokenizer
from transformers import BertTokenizer
from transformers import BertTokenizerFast
import argparse
import pandas as pd
import pickle
from tqdm import tqdm
from transformers import GPT2TokenizerFast, GPT2LMHeadModel
import logging
import numpy as np
def create_logger ( log_path) :
"""
将日志输出到日志文件和控制台
"""
logger = logging. getLogger( __name__)
logger. setLevel( logging. INFO)
formatter = logging. Formatter(
'%(asctime)s - %(levelname)s - %(message)s' )
file_handler = logging. FileHandler(
filename= log_path)
file_handler. setFormatter( formatter)
file_handler. setLevel( logging. INFO)
logger. addHandler( file_handler)
console = logging. StreamHandler( )
console. setLevel( logging. DEBUG)
console. setFormatter( formatter)
logger. addHandler( console)
return logger
def preprocess ( ) :
"""
对原始语料进行tokenize，将每段对话处理成如下形式："[CLS]utterance1[SEP]utterance2[SEP]utterance3[SEP]"
"""
parser = argparse. ArgumentParser( )
parser. add_argument( '--vocab_path' , default= 'vocab/vocab.txt' , type = str , required= False ,
help = '词表路径' )
parser. add_argument( '--log_path' , default= 'data/preprocess.log' , type = str , required= False , help = '训练日志存放位置' )
parser. add_argument( '--train_path' , default= '50w_qa_data' , type = str , required= False , help = '训练日志存放位置' )
parser. add_argument( '--save_path' , default= 'data/train.pkl' , type = str , required= False , help = 'tokenize的训练数据集' )
args = parser. parse_args( )
logger = create_logger( args. log_path)
tokenizer = BertTokenizerFast( vocab_file= args. vocab_path, sep_token= "[SEP]" , pad_token= "[PAD]" , cls_token= "[CLS]" )
sep_id = tokenizer. sep_token_id
cls_id = tokenizer. cls_token_id
logger. info( "preprocessing data,data path:{}, save path:{}" . format ( args. train_path, args. save_path) )
with open ( args. train_path, 'rb' ) as f:
data = f. read( ) . decode( "utf-8" )
if "\r\n" in data:
train_data = data. split( "\r\n\r\n" )
else :
train_data = data. split( "\n" )
logger. info( "there are {} dialogue in dataset" . format ( len ( train_data) ) )
dialogue_len = [ ]
dialogue_list = [ ]
with open ( args. save_path, "w" , encoding= "utf-8" ) as f:
for index, dialogue in enumerate ( tqdm( train_data) ) :
if "\r\n" in data:
utterances = dialogue. split( "\r\n" )
else :
utterances = dialogue. split( "\t" )
input_ids = [ cls_id]
for utterance in utterances:
input_ids += tokenizer. encode( utterance, add_special_tokens= False )
input_ids. append( sep_id)
dialogue_len. append( len ( input_ids) )
dialogue_list. append( input_ids)
with open ( args. save_path, "wb" ) as f:
pickle. dump( dialogue_list, f)
if __name__ == '__main__' :
preprocess( )
