text: import transformers
  import torch
  import os
  import json
  import random
  import numpy as np
  import argparse
  from torch.utils.tensorboard import SummaryWriter
  from datetime import datetime
  from tqdm import tqdm
  from torch.nn import DataParallel
  from tokenizations.bpe_tokenizer import get_encoder
  
  
  def build_files(data_path, tokenized_data_path, num_pieces, full_tokenizer, min_length):
      with open(data_path, 'r', encoding='utf8') as f:
          print('reading lines')
          lines = json.load(f)
          lines = [line.replace('\n', ' [SEP] ') for line in lines]  # 用[SEP]表示换行, 段落之间使用SEP表示段落结束
      all_len = len(lines)
      if not os.path.exists(tokenized_data_path):
          os.mkdir(tokenized_data_path)
      for i in tqdm(range(num_pieces)):
          sublines = lines[all_len // num_pieces * i: all_len // num_pieces * (i + 1)]
          if i == num_pieces - 1:
              sublines.extend(lines[all_len // num_pieces * (i + 1):])  # 把尾部例子添加到最后一个piece
          sublines = [full_tokenizer.tokenize(line) for line in sublines if
                      len(line) > min_length]  # 只考虑长度超过min_length的句子
          sublines = [full_tokenizer.convert_tokens_to_ids(line) for line in sublines]
          full_line = []
          for subline in sublines:
              full_line.append(full_tokenizer.convert_tokens_to_ids('[MASK]'))  # 文章开头添加MASK表示文章开始
              full_line.extend(subline)
              full_line.append(full_tokenizer.convert_tokens_to_ids('[CLS]'))  # 文章之间添加CLS表示文章结束
          with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'w') as f:
              for id in full_line:
                  f.write(str(id) + ' ')
      print('finish')
  
  
  def 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_small.json', type=str, required=False,
                          help='选择模型参数')
      parser.add_argument('--tokenizer_path', default='cache/vocab_small.txt', type=str, required=False, help='选择词库')
      parser.add_argument('--raw_data_path', default='data/train.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=5, 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('--lr', default=1.5e-4, type=float, required=False, help='学习率')
      parser.add_argument('--warmup_steps', default=2000, type=int, required=False, help='warm up步数')
      parser.add_argument('--log_step', default=1, type=int, required=False, help='多少步汇报一次loss，设置为gradient accumulation的整数倍')
      parser.add_argument('--stride', default=768, 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=int, 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=10, 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/', 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('--segment', action='store_true', help='中文以词为单位')
      parser.add_argument('--bpe_token', action='store_true', help='subword')
      parser.add_argument('--encoder_json', default="tokenizations/encoder.json", type=str, help="encoder.json")
      parser.add_argument('--vocab_bpe', default="tokenizations/vocab.bpe", type=str, help="vocab.bpe")
  
      args = parser.parse_args()
      print('args:\n' + args.__repr__())
  
      if 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
  
      model_config = transformers.modeling_gpt2.GPT2Config.from_json_file(args.model_config)
      print('config:\n' + model_config.to_json_string())
  
      n_ctx = model_config.n_ctx
      if args.bpe_token:
          full_tokenizer = get_encoder(args.encoder_json, args.vocab_bpe)
      else:
          full_tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
      full_tokenizer.model_max_length = 999999
      device = 'cuda' if torch.cuda.is_available() else 'cpu'
      print('using device:', device)
  
      raw_data_path = args.raw_data_path
      tokenized_data_path = args.tokenized_data_path
      raw = args.raw  # 选择是否从零开始构建数据集
      epochs = args.epochs
      batch_size = args.batch_size
      lr = args.lr
      warmup_steps = args.warmup_steps
      log_step = args.log_step
      stride = args.stride
      gradient_accumulation = args.gradient_accumulation
      fp16 = args.fp16  # 不支持半精度的显卡请勿打开
      fp16_opt_level = args.fp16_opt_level
      max_grad_norm = args.max_grad_norm
      num_pieces = args.num_pieces
      min_length = args.min_length
      output_dir = args.output_dir
      tb_writer = SummaryWriter(log_dir=args.writer_dir)
      assert log_step % gradient_accumulation == 0
  
      if not os.path.exists(output_dir):
          os.mkdir(output_dir)
  
      if raw:
          print('building files')
          build_files(data_path=raw_data_path, tokenized_data_path=tokenized_data_path, num_pieces=num_pieces,
                      full_tokenizer=full_tokenizer, min_length=min_length)
          print('files built')
  
      if not args.pretrained_model:
          model = transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config)
      else:
          model = transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(args.pretrained_model)
      model.train()
      model.to(device)
  
      num_parameters = 0
      parameters = model.parameters()
      for parameter in parameters:
          num_parameters += parameter.numel()
      print('number of parameters: {}'.format(num_parameters))
  
      multi_gpu = False
      full_len = 0
      print('calculating total steps')
      for i in tqdm(range(num_pieces)):
          with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
              full_len += len([int(item) for item in f.read().strip().split()])
      total_steps = int(full_len / stride * epochs / batch_size / gradient_accumulation)
      print('total steps = {}'.format(total_steps))
  
      optimizer = transformers.AdamW(model.parameters(), lr=lr, correct_bias=True)
      scheduler = transformers.WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps,
                                                            t_total=total_steps)
      if 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=fp16_opt_level)
  
      if torch.cuda.device_count() > 1:
          print("Let's use", torch.cuda.device_count(), "GPUs!")
          model = DataParallel(model, device_ids=[int(i) for i in args.device.split(',')])
          multi_gpu = True
      print('starting training')
      overall_step = 0
      running_loss = 0
      for epoch in range(epochs):
          print('epoch {}'.format(epoch + 1))
          now = datetime.now()
          print('time: {}'.format(now))
          x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
          random.shuffle(x)
          piece_num = 0
          for i in x:
              with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
                  line = f.read().strip()
              tokens = line.split()
              tokens = [int(token) for token in tokens]
              start_point = 0
              samples = []
              while start_point < len(tokens) - n_ctx:
                  samples.append(tokens[start_point: start_point + n_ctx])
                  start_point += stride
              if start_point < len(tokens):
                  samples.append(tokens[len(tokens)-n_ctx:])
              random.shuffle(samples)
              for step in range(len(samples) // batch_size):  # drop last
  
                  #  prepare data
                  batch = samples[step * batch_size: (step + 1) * batch_size]
                  batch_inputs = []
                  for ids in batch:
                      int_ids = [int(x) for x in ids]
                      batch_inputs.append(int_ids)
                  batch_inputs = torch.tensor(batch_inputs).long().to(device)
  
                  #  forward pass
                  outputs = model.forward(input_ids=batch_inputs, labels=batch_inputs)
                  loss, logits = outputs[:2]
  
                  #  get loss
                  if multi_gpu:
                      loss = loss.mean()
                  if gradient_accumulation > 1:
                      loss = loss / gradient_accumulation
  
                  #  loss backward
                  if fp16:
                      with amp.scale_loss(loss, optimizer) as scaled_loss:
                          scaled_loss.backward()
                          torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), max_grad_norm)
                  else:
                      loss.backward()
                      torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
  
                  #  optimizer step
                  if (overall_step + 1) % gradient_accumulation == 0:
                      running_loss += loss.item()
                      optimizer.step()
                      optimizer.zero_grad()
                      scheduler.step()
                  if (overall_step + 1) % log_step == 0:
                      tb_writer.add_scalar('loss', loss.item() * gradient_accumulation, overall_step)
                      print('now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'.format(
                          datetime.now().hour,
                          datetime.now().minute,
                          step + 1,
                          piece_num,
                          epoch + 1,
                          running_loss * gradient_accumulation / (log_step / gradient_accumulation)))
                      running_loss = 0
                  overall_step += 1
              piece_num += 1
  
          print('saving model for epoch {}'.format(epoch + 1))
          if not os.path.exists(output_dir + 'model_epoch{}'.format(epoch + 1)):
              os.mkdir(output_dir + 'model_epoch{}'.format(epoch + 1))
          model_to_save = model.module if hasattr(model, 'module') else model
          model_to_save.save_pretrained(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))
          print('epoch {} finished'.format(epoch + 1))
  
          then = datetime.now()
          print('time: {}'.format(then))
          print('time for one epoch: {}'.format(then - now))
  
      print('training finished')
      if not os.path.exists(output_dir + 'final_model'):
          os.mkdir(output_dir + 'final_model')
      model_to_save = model.module if hasattr(model, 'module') else model
      model_to_save.save_pretrained(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__':
      main()
  