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train_toutiao.py
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train_toutiao.py
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import pytorch_transformers
from model import GPT2KWModel
import torch
from torch.utils import data
import os
import json
import re
import random
import numpy as np
import argparse
# from torch.utils.tensorboard import SummaryWriter
from datetime import datetime
from torch.nn import DataParallel
import time
class GPT2Trainer:
def __init__(self, args, debug_mode=False):
if args.no_wordpiece:
from tokenizations import tokenization_bert_without_wordpiece as tokenization_bert
elif args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
from tokenizations import tokenization_bert
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
self.model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file(args.model_config)
self.n_ctx = self.model_config.n_ctx
self.full_tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
self.full_tokenizer.max_len = self.n_ctx
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.raw_data_path = args.raw_data_path
self.tokenized_data_path = args.tokenized_data_path
self.raw = args.raw # 选择是否从零开始构建数据集
self.epochs = args.epochs
self.batch_size = args.batch_size
self.lr = args.lr
self.warmup_steps = args.warmup_steps
self.log_step = args.log_step
self.stride = args.stride
self.gradient_accumulation = args.gradient_accumulation
self.fp16 = args.fp16 # 不支持半精度的显卡请勿打开
self.fp16_opt_level = args.fp16_opt_level
self.max_grad_norm = args.max_grad_norm
self.num_pieces = args.num_pieces
self.min_length = args.min_length
self.output_dir = args.output_dir
self.pretrained_model = args.pretrained_model
self.accumulation_steps = args.accumulation_steps
# self.tb_writer = SummaryWriter(log_dir=args.writer_dir)
self.debug_mode = debug_mode
self.keywords_max_length = 64
self.passage_max_length = 256
self.passage_min_length = 128
self.f_log = open("train_log.txt", "w")
def process_data_dict(self, data_dict):
title = data_dict["title"]
content = data_dict["content"]
keywords = data_dict["keywords"]
sorted_keywords = sorted(keywords.items(), key=lambda x: x[1], reverse=True)
keywords = list(map(lambda x: x[0].strip(), sorted_keywords))
keywords = list(filter(lambda x: len(x) > 1, keywords))
passage_ids_list = []
keyword_ids_list = []
if len(content) > self.min_length and len(keywords) > 0:
# 如果标题为空字符,不要加上\n
passage = title + "\n" + content if len(title) > 0 else content
passage = passage.replace('\n', ' [SEP] ')
passage = ' [MASK] ' + passage + ' [CLS] ' # [MASK] 表示文章开头,[CLS] 表示文章结束
passage_tokens = self.full_tokenizer.tokenize(passage)
keyword_tokens_list = [self.full_tokenizer.tokenize(keyword) for keyword in keywords]
# 按 stride 分割文章
start_point = 0
sample_passage_list = []
sample_keywords_list = []
while start_point < len(passage_tokens) - self.passage_max_length:
sample_passage = passage_tokens[start_point: start_point + self.passage_max_length]
# sample_keywords = list(filter(lambda x: " ".join(x) in " ".join(sample_passage), keyword_tokens_list))
sample_keywords = keyword_tokens_list
sample_passage_list.append(sample_passage)
sample_keywords_list.append(sample_keywords)
start_point += self.stride
else:
sample_passage = passage_tokens[-self.passage_max_length:]
# sample_keywords = list(filter(lambda x: " ".join(x) in " ".join(sample_passage), keyword_tokens_list))
sample_keywords = keyword_tokens_list
sample_passage_list.append(sample_passage)
sample_keywords_list.append(sample_keywords)
# 把 token 变 id
for sample_passage, sample_keywords in zip(sample_passage_list, sample_keywords_list):
passage_ids = self.full_tokenizer.convert_tokens_to_ids(sample_passage + ["[PAD]"] * (self.passage_max_length - len(sample_passage)))
keyword_ids = []
for keyword_tokens in sample_keywords:
single_keyword_ids = self.full_tokenizer.convert_tokens_to_ids(keyword_tokens + ["[SEP]"])
if len(keyword_ids) + len(single_keyword_ids) < self.keywords_max_length:
keyword_ids.extend(single_keyword_ids)
keyword_ids.extend(self.full_tokenizer.convert_tokens_to_ids(["[PAD]"] * (self.keywords_max_length - len(keyword_ids))))
passage_ids_list.append(passage_ids)
keyword_ids_list.append(keyword_ids)
return keyword_ids_list, passage_ids_list
def create_dataloader(self):
total_keyword_ids_list = []
total_passage_ids_list = []
with open(self.raw_data_path, "r") as f:
for i, line in enumerate(f):
if self.debug_mode and i == 200: break
data_dict = json.loads(line)
if (i + 1) % 10000 == 0: self.print_and_log("已加载训练样本 %d" % (i + 1))
keyword_ids_list, passage_ids_list = self.process_data_dict(data_dict)
if len(keyword_ids_list) != 0 and len(passage_ids_list) != 0:
total_keyword_ids_list.extend(keyword_ids_list)
total_passage_ids_list.extend(passage_ids_list)
# 打乱,划分训练集和验证集
random.seed(1234)
random.shuffle(total_keyword_ids_list)
random.seed(1234)
random.shuffle(total_passage_ids_list)
valid_num = int(len(total_keyword_ids_list) * 0.02)
train_keyword_ids_list = total_keyword_ids_list[:-valid_num]
train_passage_ids_list = total_passage_ids_list[:-valid_num]
valid_keyword_ids_list = total_keyword_ids_list[-valid_num:]
valid_passage_ids_list = total_passage_ids_list[-valid_num:]
# 建立 dataset
train_dataset = data.TensorDataset(torch.tensor(train_keyword_ids_list, dtype=torch.long),
torch.tensor(train_passage_ids_list, dtype=torch.long))
valid_dataset = data.TensorDataset(torch.tensor(valid_keyword_ids_list, dtype=torch.long),
torch.tensor(valid_passage_ids_list, dtype=torch.long))
if torch.cuda.is_available():
pin_memory = True
num_workers = 7
else:
pin_memory = False
num_workers = 3
# 建立 dataloader
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True, pin_memory=pin_memory, num_workers=num_workers)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=self.batch_size, shuffle=False, pin_memory=pin_memory, num_workers=num_workers)
return train_loader, valid_loader
def print_and_log(self, text):
print(text)
self.f_log.write(text + "\n")
self.f_log.flush()
def train(self):
if not self.pretrained_model:
model = GPT2KWModel(config=self.model_config)
else:
self.print_and_log('加载预训练模型')
model = GPT2KWModel.from_pretrained(self.pretrained_model)
model.train()
model.to(self.device)
# 计算模型参数量
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
self.print_and_log('模型参数量: {}'.format(num_parameters))
self.print_and_log("开始加载训练集")
train_loader, valid_loader = self.create_dataloader()
self.print_and_log("训练集加载完毕")
epoch_steps = int(train_loader.sampler.num_samples / self.batch_size / self.accumulation_steps)
total_steps = epoch_steps * self.epochs
self.print_and_log('总样本数 = {}'.format(train_loader.sampler.num_samples))
self.print_and_log('epoch 步数 = {}'.format(epoch_steps))
self.print_and_log('总步数 = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(), lr=self.lr, correct_bias=True)
# scheduler = pytorch_transformers.WarmupLinearSchedule(optimizer, warmup_steps=self.warmup_steps, t_total=total_steps)
scheduler = pytorch_transformers.WarmupCosineSchedule(optimizer, warmup_steps=self.warmup_steps, t_total=total_steps)
if self.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=self.fp16_opt_level)
if torch.cuda.device_count() > 1:
model = DataParallel(model)
multi_gpu = True
else:
multi_gpu = False
overall_step = 0
running_loss = 0
model.train()
for epoch in range(self.epochs):
self.print_and_log('epoch {}'.format(epoch + 1))
now = datetime.now()
self.print_and_log('time: {}'.format(now))
optimizer.zero_grad()
for i, batch_data in enumerate(train_loader):
if torch.cuda.is_available():
keyword_ids = batch_data[0].to(self.device, non_blocking=True)
passage_ids = batch_data[1].to(self.device, non_blocking=True)
label_ids = passage_ids.clone().to(self.device, non_blocking=True)
else:
keyword_ids = batch_data[0]
passage_ids = batch_data[1]
label_ids = passage_ids.clone()
outputs = model(input_ids=passage_ids, keyword_ids=keyword_ids, labels=label_ids)
loss, logits = outputs[:2]
# 多 GPU 训练
if multi_gpu:
loss = loss.mean()
# 梯度累加
if self.gradient_accumulation > 1:
loss = loss / self.gradient_accumulation
# 混合精度训练或正常训练
if self.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), self.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), self.max_grad_norm)
# 更新权重
if (i + 1) % self.gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
overall_step += 1
# 报告 train loss
if (overall_step + 1) % self.log_step == 0 and running_loss != 0:
self.print_and_log('now time: {}:{}. Step {} of epoch {}, loss {}'.format(
datetime.now().hour,
datetime.now().minute,
overall_step + 1,
epoch + 1,
running_loss * self.gradient_accumulation / self.log_step))
running_loss = 0
# 开始验证
with torch.no_grad():
valid_start_time = datetime.now()
model.eval()
valid_loss = 0
valid_step = 0
for i, valid_batch_data in enumerate(valid_loader):
if torch.cuda.is_available():
keyword_ids = valid_batch_data[0].to(self.device, non_blocking=True)
passage_ids = valid_batch_data[1].to(self.device, non_blocking=True)
label_ids = passage_ids.clone().to(self.device, non_blocking=True)
else:
keyword_ids = valid_batch_data[0]
passage_ids = valid_batch_data[1]
label_ids = passage_ids.clone()
outputs = model(input_ids=passage_ids, keyword_ids=keyword_ids, labels=label_ids)
loss, logits = outputs[:2]
valid_loss += loss
valid_step += 1
valid_loss = valid_loss / valid_step
self.print_and_log('valid duration: {}, valid loss: {}'.format(datetime.now() - valid_start_time, valid_loss))
# 保存模型
if (epoch + 1) % 1 == 0:
if not os.path.exists(self.output_dir + 'model_epoch{}'.format(epoch + 1)):
os.makedirs(self.output_dir + 'model_epoch{}'.format(epoch + 1))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(self.output_dir + 'model_epoch{}'.format(epoch + 1))
# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
then = datetime.now()
self.print_and_log('time: {}'.format(then))
self.print_and_log('time for one epoch: {}'.format(then - now))
model.train()
self.print_and_log('training finished')
self.f_log.close()
if not os.path.exists(self.output_dir + 'final_model'):
os.makedirs(self.output_dir + 'final_model')
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(self.output_dir + 'final_model')
# torch.save(scheduler.state_dict(), output_dir + 'final_model/scheduler.pt')
# torch.save(optimizer.state_dict(), output_dir + 'final_model/optimizer.pt')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='0,1,2,3', type=str, required=False, help='设置使用哪些显卡')
parser.add_argument('--model_config', default='config/model_config.json', type=str, required=False, help='选择模型参数')
parser.add_argument('--tokenizer_path', default='cache/vocab.txt', type=str, required=False, help='选择词库')
parser.add_argument('--raw_data_path', default='data/train_toutiao.json', type=str, required=False, help='原始训练语料')
parser.add_argument('--tokenized_data_path', default='data/tokenized/', type=str, required=False, help='tokenized语料存放位置')
parser.add_argument('--raw', action='store_true', help='是否先做tokenize')
parser.add_argument('--epochs', default=8, type=int, required=False, help='训练循环')
parser.add_argument('--batch_size', default=8, type=int, required=False, help='训练batch size')
parser.add_argument('--accumulation_steps', default=1, type=int, required=False, help='梯度累加')
parser.add_argument('--lr', default=6e-5, type=float, required=False, help='学习率')
parser.add_argument('--warmup_steps', default=10000, type=int, required=False, help='warm up步数')
parser.add_argument('--log_step', default=10000, type=int, required=False, help='多少步汇报一次loss')
parser.add_argument('--stride', default=192, type=int, required=False, help='训练时取训练数据的窗口步长')
parser.add_argument('--gradient_accumulation', default=1, type=str, required=False, help='梯度积累')
parser.add_argument('--fp16', action='store_true', help='混合精度')
parser.add_argument('--fp16_opt_level', default='O1', type=str, required=False)
parser.add_argument('--max_grad_norm', default=1.0, type=float, required=False)
parser.add_argument('--num_pieces', default=100, type=int, required=False, help='将训练语料分成多少份')
parser.add_argument('--min_length', default=128, type=int, required=False, help='最短收录文章长度')
parser.add_argument('--output_dir', default='model_toutiao_256/', type=str, required=False, help='模型输出路径')
parser.add_argument('--pretrained_model', default='', type=str, required=False, help='模型训练起点路径')
parser.add_argument('--writer_dir', default='tensorboard_summary/', type=str, required=False, help='Tensorboard路径')
parser.add_argument('--no_wordpiece', action='store_true', help='不做word piece切词')
parser.add_argument('--segment', action='store_true', help='中文以词为单位')
args = parser.parse_args()
if os.path.exists("/Volumes/移动硬盘/model/GPT2_pretrained"):
args.fp16 = False
args.raw_data_path = "/Volumes/移动硬盘/数据/头条历史/train_toutiao.json"
args.pretrained_model = "/Volumes/移动硬盘/model/GPT2_pretrained"
else:
args.fp16 = False
args.raw_data_path = "train_toutiao_all.json"
args.pretrained_model = "/root/text_generation/model_toutiao/final_model"
# args.pretrained_model = ""
trainer = GPT2Trainer(args, debug_mode=False)
auto_shutdown = True
if auto_shutdown:
try:
trainer.train()
except:
pass
os.system("sudo init 0")
else:
trainer.train()