開場

最近chatgpt已經火爆了，幾乎是家喻戶曉老少皆知啊，公測推出60天后就已經是UV人數過億，日訪問量號稱也是過億。投資chatgpt研發團隊的微軟也是2個月內迅速推出自己的chatgpt的bing搜索，股票下載量都是暴增啊。前面文章已經介紹過chatgpt技術可能會對整個人類組織分工帶來的影響以及原因，這里就不在繼續歪歪了。

chatgpt的一些思考

從這篇文章開始，我打算實現一個mini版本的chatgpt，把背后的原理算法、數據準備工作都會介紹到。這系列文章預計會有7-8篇，主要是講實現，不會介紹transformer模型技術細節、ppo數學推理。

到最后大家可以收獲一個問答式的文本生成工具，大家也可以根據自己需要定制訓練自己的模型做自己想要做的事，比如一個跟懂自己智能助理、解讀論文的神器、可以通過語音方式理解需求幫你控制智能家居、通過語音幫你畫一幅你想要的畫...

第一篇先介紹整個RLHF大訓練框架，介紹SFT模型訓練：數據、基本模型。先介紹單個模型大家先熟悉代碼在自己機器上試跑訓練下數據。

第二部分會對模型改造、代碼封裝，讓代碼能夠在多卡多機上訓練；更工業風。

第三部分把流程封裝，三部分的代碼做一個整合，到這邊你就可以得到一個真正能夠訓練中文語料的鏈路框架，并且可以自己準備訓練標注語料。

第四部分會給大家介紹基于這個小的chatgpt引擎做的各種應用探索。

宏觀介紹

整個鏈路包括三塊：

文本生成AGGENT，為了得到一個不錯Agent我們需要用‘輸入-輸出’語料對訓練一個不錯基準模型，把這個過程叫做sft

評判文本生成好壞的Reward，為了得到Reward模型我們需要用‘輸入-輸出list’語料做一個排序打分模型，把這個過程叫做Reward

利用Reward反饋調試Agent模型PPO調控器

fig1.sft訓練過程

fig2.reward訓練過程

Rank數據打標

SFT實現

先訓練一個基本的有文本生成能力的模型，可以選用GPT或者T5框架模型來做訓練。

from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-lyric") model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-lyric") text_generator = TextGenerationPipeline(model, tokenizer) text_generator("最美的不是下雨天，是曾與你躲過雨的屋檐", max_length=100, do_sample=True)

GPT2

數據預處理部分

數據樣式：

數據使用了deepmind整理的cnnstory部分數據：

原始數據樣式如下：

(CNN)Syria is a Hell on Earth that is expanding in plain sight.The death toll there has doubled in a year's time, if an opposition group is right.Since civil war broke out there, 310,000 people have been killed, the Syrian Observatory for Human Rights said Thursday. A year earlier, SOHR's tally stood at 162,402. And the year before, the United Nations put the death toll at 70,000.Violence has plunged well over half of all Syrians into such destitution that they are in dire need of survival aid, the United Nations says, as food rations are being cut for lack of donations.Numbers alone can't convey the immeasurable anguish of millions, but maybe it can remind the rest of us of the magnitude of the world's currently greatest tragedy.The number of years since perpetual bloodshed began, since dictator Bashar al-Assad's security forces fired on crowds of demonstrators and armed militant groups rose up against him in March 2011.Percentage of the Syrian population killed. It would be like killing 3 to 4 million Americans. The range comes from the SOHR's death toll of 310,000 and a recent lower estimate by the U.N. of at least 220,000 dead.The number of Syrians in need of immediate life-saving aid, according to the U.N. That's the population of Moscow.Syrians driven from their homes, the U.N. says. Imagine the entire Boston metropolitan area emptied out.Syrians who have fled as refugees to neighboring countries, creating humanitarian and economic hardship across Syria's borders. Turkey has taken in 1.7 million, Lebanon 1.2 million, Jordan 625,000, and Iraq 245,000.The reduction in the size of food rations the World Food Programme says it has been forced to make due to a lack of donations. That means people receiving aid will get only 60% of the daily nutrition they need.@highlightMore people have been displaced than live in Moscow; more people lost their homes than live in greater Boston@highlightThe WFP has cut food ration sizes by 30% for lack of donations

上面數據@highlight部分就是文章的摘要部分

#這個文件命名為until.py， import random import numpy as np import torch import torch.nn.functional as F from transformers import GPT2Tokenizer from tqdm import tnrange#下面方法主要用來做gptencode def add_special_tokens():""" Returns GPT2 tokenizer after adding separator and padding tokens """tokenizer = GPT2Tokenizer.from_pretrained('gpt2')special_tokens = {'pad_token':'<|pad|>','sep_token':'<|sep|>'}num_add_toks = tokenizer.add_special_tokens(special_tokens)return tokenizerdef set_seed(args):random.seed(args.seed)np.random.seed(args.seed)torch.manual_seed(args.seed)if args.n_gpu > 0:torch.cuda.manual_seed_all(args.seed)def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):""" Filter a distribution of logits using top-k and/or nucleus (top-p) filteringArgs:logits: logits distribution shape (vocabulary size)top_k > 0: keep only top k tokens with highest probability (top-k filtering).top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317"""assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less cleartop_k = min(top_k, logits.size(-1)) # Safety checkif top_k > 0:# Remove all tokens with a probability less than the last token of the top-kindices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]logits[indices_to_remove] = filter_valueif top_p > 0.0:sorted_logits, sorted_indices = torch.sort(logits, descending=True)cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)# Remove tokens with cumulative probability above the thresholdsorted_indices_to_remove = cumulative_probs > top_p# Shift the indices to the right to keep also the first token above the thresholdsorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()sorted_indices_to_remove[..., 0] = 0indices_to_remove = sorted_indices[sorted_indices_to_remove]logits[indices_to_remove] = filter_valuereturn logitsdef sample_seq(model, context, length, device, temperature=1, top_k=0, top_p=0.0):""" Generates a sequence of tokens Args:model: gpt/gpt2 modelcontext: tokenized text using gpt/gpt2 tokenizerlength: length of generated sequence.device: torch.device object.temperature >0: used to control the randomness of predictions by scaling the logits before applying softmax.top_k > 0: keep only top k tokens with highest probability (top-k filtering).top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering)."""context = torch.tensor(context, dtype=torch.long, device=device)context = context.unsqueeze(0)generated = contextwith torch.no_grad(): for _ in tnrange(length):inputs = {'input_ids': generated}outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)next_token_logits = outputs[0][0, -1, :] / temperaturefiltered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)return generateddef beam_search(model, context, length, beam_size, device, temperature=1):""" Generate sequence using beam search https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/Args:model: gpt/gpt2 modelcontext: tokenized text using gpt/gpt2 tokenizerlength: length of generated sequence.beam_size: >=1 and <= total_no_of_tokensdevice: torch.device object.temperature >0: used to control the randomness of predictions by scaling the logits before applying softmax."""context = torch.tensor(context, dtype=torch.long, device=device)context = context.unsqueeze(0)with torch.no_grad(): inputs = {'input_ids': context}outputs = model(**inputs) next_token_logits = outputs[0][0, -1, :] / temperaturenext_token_probs = F.softmax(next_token_logits)scores, indices = torch.topk(next_token_probs, beam_size)indices = indices.tolist()sequences = [[c] for c in indices]for _ in tnrange(length-1):logits = torch.zeros(beam_size*len(next_token_logits))for j in range(len(sequences)):new_generated = torch.cat((context,torch.tensor([sequences[j]], dtype=torch.long, device=device)),dim=1)inputs = {'input_ids': new_generated}outputs = model(**inputs) next_token_logits = outputs[0][0, -1, :] / temperaturenext_token_probs = F.softmax(next_token_logits)start, stop = j*len(next_token_logits), (j+1)*len(next_token_logits)logits[start:stop] = scores[j]*next_token_probsscores, new_logits_indices = torch.topk(logits,beam_size)logits = (new_logits_indices%50259).tolist()for j in range(len(sequences)):sequences[j] = sequences[j]+[logits[j]]return scores, sequencesdef generate_beam_sample(data, tokenizer, model, num=1, length=100, beam_size=3, device=torch.device('cuda')):""" Generate summaries for "num" number of articles using beam search.Args:data = GPT21024Dataset objecttokenizer = gpt/gpt2 tokenizernum = number of articles for which summaries has to be generated"""for i in range(num):sample = data[i]idx = sample['sum_idx']context = sample['article'][:idx].tolist()summary = sample['article'][idx+1:][:100].tolist()scores, sequences = beam_search(model, context, length, beam_size, device)print('new_article', end='\n\n')print(tokenizer.decode(context[:-1]), end='\n\n')print('actual_summary', end='\n\n')print(tokenizer.decode(summary), end='\n\n')for i in range(len(sequences)):text = tokenizer.convert_ids_to_tokens(sequences[i],skip_special_tokens=True)text = tokenizer.convert_tokens_to_string(text) print("generated_summary-{} and Score is {}.".format(i+1, scores[i]), end='\n\n')print(text, end='\n\n')def generate_sample(data, tokenizer, model, num=1, eval_step=False, length=100, temperature=1, top_k=10, top_p=0.5, device=torch.device('cuda')):""" Generate summaries for "num" number of articles.Args:data = GPT21024Dataset objecttokenizer = gpt/gpt2 tokenizermodel = gpt/gpt2 modelnum = number of articles for which summaries has to be generatedeval_step = can be True/False, checks generating during evaluation or not"""for i in range(num):sample = data[i]idx = sample['sum_idx']context = sample['article'][:idx].tolist()summary = sample['article'][idx+1:][:100].tolist()generated_text = sample_seq(model, context, length, device, temperature, top_k, top_p)generated_text = generated_text[0, len(context):].tolist()text = tokenizer.convert_ids_to_tokens(generated_text,skip_special_tokens=True)text = tokenizer.convert_tokens_to_string(text)if eval_step==False:print('new_article', end='\n\n')print(tokenizer.decode(context), end='\n\n')print("generated_summary", end='\n\n')print(text, end='\n\n')print('actual_summary', end='\n\n')print(tokenizer.decode(summary), end='\n\n')else:print(tokenizer.decode(context), end='\n\n')print("generated_summary", end='\n\n')

把數據轉成一篇文章對應一個json文件格式，json包括article、abstract兩部分，同時對文本做gptencode編碼處理代碼如下：

import json import os import pickle import sys import timefrom utils import add_special_tokens#tokenizer = GPT2Tokenizer.from_pretrained('gpt2') dm_single_close_quote = '\u2019' # unicode dm_double_close_quote = '\u201d' # acceptable ways to end a sentence END_TOKENS = ['.', '!', '?', '...', "'", "`", '"',dm_single_close_quote, dm_double_close_quote, ")"]def fix_missing_period(line):"""Adds a period to a line that is missing a period"""if "@highlight" in line:return lineif line == "":return lineif line[-1] in END_TOKENS:return linereturn line + " ."def get_art_abs(lines):""" return as list of sentences"""# truncated trailing spaces, and normalize spaceslines = [' '.join(line.strip().split()) for line in lines]lines = [fix_missing_period(line) for line in lines]# Separate out article and abstract sentencesarticle_lines = []highlights = []next_is_highlight = Falsefor idx, line in enumerate(lines):if line == "":continue # empty lineelif line.startswith("@highlight"):next_is_highlight = Trueelif next_is_highlight:highlights.append(line)else:article_lines.append(line)return ' '.join(article_lines), ' '.join(highlights)def write_json(i,article, abstract):""" Saves a json file."""file = "./gpt2_1024_data/"+str(i)+".json"js_example = {}js_example['id'] = ijs_example['article'] = articlejs_example['abstract'] = abstractwith open(file, 'w') as f:json.dump(js_example, f, ensure_ascii=False)def main(file_names, directory):""" Reads txt files, extract articles and summaries, tokenize them and save as json filesArgs:file_names: list, all the articles with total no of tokens less than 1024directory: string, directory where files in file_names is stored"""tokenizer = add_special_tokens()print("Execution Started...")train_ids = []file_id_map = {}i = 0for file in file_names:file = os.path.join(os.getcwd(),directory,file)with open(file,'r',encoding='utf-8') as f:lines = f.read().split('\n\n')article, abstract = get_art_abs(lines)article, abstract = tokenizer.encode(article), tokenizer.encode(abstract)if len(article)>0 and len(abstract)>0 and (len(article)+len(abstract))<=1023:train_ids.append(i)write_json(i,article,abstract)file_id_map[i] = os.path.basename(file).replace('.story', '')i += 1if i%100==0:print(i, " files written")x,y = int(len(train_ids)*0.8), int(len(train_ids)*0.9)valid_ids = train_ids[x:y]test_ids = train_ids[y:]train_ids = train_ids[:x]with open("ids.json",'w') as f:js = {}js['train_ids'] = train_idsjs['valid_ids'] = valid_idsjs['test_ids'] = test_idsjson.dump(js,f)# file_id_map maps the json file ids to actual cnn/dm file names ending with ".story"print("saving file_id_map...")with open("file_id_map.pickle", 'wb') as f:pickle.dump(file_id_map,f)print("file_id_map saved.")if __name__ == '__main__':start = time.time()with open(sys.argv[1],'rb') as f:file_sizes = pickle.load(f)file_names = [file for file,size in file_sizes.items() if size<=1023] #only consider files with total no of tokens less than 1024if sys.argv[1].startswith("cnn"):directory = "cnn_stories_tokenized"os.chdir('/CNN/')else:directory = "dm_stories_tokenized"os.chdir('./DM/')main(file_names, directory)print("total_time_taken: ", (time.time()-start)/60, " minutes")

處理完的數據格式如下

{"id": 0, "article": [12, 43, 27912, 12, 8100, 532, 21095, 33, 12, 1377, 7214, 4621, 286, 262, 890, 5041, 351, 257, 474, 5978, 284, 534, 17627, 764, 775, 1965, 1312, 6207, 3816, 284, 2648, 5205, 286, 511, 4004, 7505, 3952, 5636, 2171, 764], "abstract": [9787, 503, 8100, 13, 785, 7183, 705, 7505, 3952, 5205, 764, 1471, 19550, 287, 319, 262, 995, 705, 82, 27627, 6386, 1660, 19392, 764]}

---

模型訓練部分

#這部分代碼拷貝命名'dataset.py' import os import json import numpy as np import torch from torch.utils.data import Datasetfrom utils import add_special_tokensclass GPT21024Dataset(Dataset):def __init__(self, root_dir, ids_file, mode='train',length=None):self.root_dir = root_dirself.tokenizer = add_special_tokens()# with open(ids_file,'r') as f:# if mode=='train':# self.idxs = np.array(json.load(f)['train_ids'])# elif mode=='valid':# self.idxs = np.array(json.load(f)['valid_ids'])# elif mode=='test':# self.idxs = np.array(json.load(f)['test_ids'])# self.idxs = self.idxs -min(self.idxs)self.idxs = os.listdir(root_dir)self.mode = modeif len == None:self.len = len(self.idxs)else:self.len = lengthdef __len__(self):return self.lendef __getitem__(self,idx):if self.mode=='valid':idx = self.idxs[-idx]elif self.mode=='test':idx = self.idxs[-idx-self.len] # assuming valid and test set of same sizeselse:idx = self.idxs[idx]# file_name = os.path.join(self.root_dir,str(idx)+".json")file_name = os.path.join(self.root_dir,str(idx))with open(file_name,'r') as f:data = json.load(f)text = self.tokenizer.encode(self.tokenizer.pad_token)*1024content = data['article'] + self.tokenizer.encode(self.tokenizer.sep_token) + data['abstract']text[:len(content)] = contenttext = torch.tensor(text)sample = {'article': text, 'sum_idx': len(data['article'])}return sample #訓練部分代碼 import argparse from datetime import datetime import os import timeimport numpy as np from transformers import GPT2LMHeadModel,AdamW, WarmupLinearSchedule from torch.utils.tensorboard import SummaryWriter import torch from torch.nn import CrossEntropyLoss import torch.nn.functional as F from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from tqdm import tnrange, tqdm_notebookfrom dataset import GPT21024Dataset from utils import add_special_tokens, generate_sample, set_seed#please change default arguments if neededparser = argparse.ArgumentParser() parser.add_argument("--lr",default=5e-5, type=float, help="learning rate") parser.add_argument("--seed",default=42, type=int, help="seed to replicate results") parser.add_argument("--n_gpu",default=1, type=int, help="no of gpu available") parser.add_argument("--gradient_accumulation_steps",default=2, type=int, help="gradient_accumulation_steps") parser.add_argument("--batch_size",default=1, type=int, help="batch_size") parser.add_argument("--num_workers",default=4, type=int, help="num of cpus available") parser.add_argument("--device",default=torch.device('cpu'), help="torch.device object") parser.add_argument("--num_train_epochs",default=1, type=int, help="no of epochs of training") parser.add_argument("--output_dir",default='./output', type=str, help="path to save evaluation results") parser.add_argument("--model_dir",default='./weights', type=str, help="path to save trained model") parser.add_argument("--max_grad_norm",default=1.0, type=float, help="max gradient norm.") parser.add_argument("--root_dir",default='./CNN/gpt2_1024_data', type=str, help="location of json dataset.") parser.add_argument("--ids_file",default='./CNN/ids.json', type=str, help="location of train, valid and test file indexes") args = parser.parse_args([]) print(args)def train(args, model, tokenizer, train_dataset, valid_dataset, ignore_index):""" Trains GPT2 model and logs necessary details.Args:args: dict that contains all the necessary information passed by user while trainingmodel: finetuned gpt/gpt2 modeltokenizer: GPT/GPT2 tokenizertrain_dataset: GPT21024Dataset object for training dataignore_index: token not considered in loss calculation"""writer = SummaryWriter('./output/logs')train_sampler = RandomSampler(train_dataset)train_dl = DataLoader(train_dataset,sampler=train_sampler,batch_size=args.batch_size,num_workers=args.num_workers)loss_fct = CrossEntropyLoss(ignore_index=ignore_index) #ignores padding token for loss calculationoptimizer = AdamW(model.parameters(),lr=args.lr)scheduler = WarmupLinearSchedule(optimizer,100,80000)global_step = 0tr_loss, logging_loss = 0.0, 0.0model.zero_grad()train_iterator = tnrange(int(args.num_train_epochs), desc="Epoch")set_seed(args)for _ in train_iterator:epoch_iterator = tqdm_notebook(train_dl, desc="Training")for step, batch in enumerate(epoch_iterator):inputs, labels = batch['article'].to(args.device), batch['article'].to(args.device)model.train()logits = model(inputs)[0]# only consider loss on reference summary just like seq2seq modelsshift_logits = logits[..., batch['sum_idx']:-1, :].contiguous()shift_labels = labels[..., batch['sum_idx']+1:].contiguous()loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))loss = loss/args.gradient_accumulation_stepsloss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)tr_loss += loss.item()if (step + 1) % args.gradient_accumulation_steps == 0:optimizer.step()scheduler.step() # Update learning rate schedulemodel.zero_grad()global_step += 1writer.add_scalar('lr', scheduler.get_lr()[0], global_step)writer.add_scalar('loss', (tr_loss - logging_loss)/args.gradient_accumulation_steps, global_step)logging_loss = tr_lossprint("loss:", loss.item(), end='\n\n')if (step + 1)/args.gradient_accumulation_steps == 1.0:print('After 1st update: ', end='\n\n')generate_sample(valid_dataset, tokenizer, model, num=2, eval_step=False,device=args.device)if (step + 1) % (10*args.gradient_accumulation_steps) == 0:results = evaluate(args, model, valid_dataset, ignore_index, global_step)for key, value in results.items():writer.add_scalar('eval_{}'.format(key), value, global_step)print('After', global_step+1,'updates: ', end='\n\n')generate_sample(valid_dataset, tokenizer, model, num=2, eval_step=True,device=args.device)# creating training and validation dataset objecttrain_data = GPT21024Dataset(args.root_dir,args.ids_file,mode='train',length=3000) #training on only 3000 datasets valid_data = GPT21024Dataset(args.root_dir,args.ids_file,mode='valid',length=500) #validation on only 500 datasets# load pretrained GPT2 tokenizer = add_special_tokens() ignore_idx = tokenizer.pad_token_id model = GPT2LMHeadModel.from_pretrained('gpt2') model.resize_token_embeddings(len(tokenizer)) model.to(args.device)#training the modelstart = time.time() train(args, model, tokenizer, train_data, valid_data, ignore_idx) print('total time: ', (time.time()-start)/60, " minutes", end='\n\n')print('Saving trained model...') model_file = os.path.join(args.model_dir, 'model_data{}_trained_after_{}_epochs_only_sum_loss_ignr_pad.bin'.format(len(train_data),args.num_train_epochs)) config_file = os.path.join(args.model_dir, 'config_data{}_trained_after_{}_epochs_only_sum_loss_ignr_pad.json'.format(len(train_data),args.num_train_epochs)) torch.save(model.state_dict(), model_file) model.config.to_json_file(config_file)

訓練好的模型做inference

import argparse import osfrom bs4 import BeautifulSoup from googlesearch import search import numpy as np import requests from transformers import GPT2Config, GPT2LMHeadModel import torch from tqdm import tnrange, tqdm_notebookfrom dataset import GPT21024Dataset from utils import add_special_tokens, beam_search, generate_beam_sample, generate_sample, sample_seq, set_seed, top_k_top_p_filtering#please change default arguments if neededparser = argparse.ArgumentParser()parser.add_argument("--seed",default=42, type=int, help="seed to replicate results") parser.add_argument("--num_workers",default=4, type=int, help="num of cpus available") parser.add_argument("--device",default=torch.device('cuda'), help="torch.device object") parser.add_argument("--output_dir",default='./output', type=str, help="path to save evaluation results") parser.add_argument("--model_dir",default='./weights', type=str, help="path to save trained model") parser.add_argument("--root_dir",default='./CNN/gpt2_1024_data', type=str, help="location of json dataset.") parser.add_argument("--ids_file",default='./CNN/ids.json', type=str, help="location of train, valid and test file indexes") args = parser.parse_args([]) print(args)# using the same validation and training data as during training tokenizer = add_special_tokens() # train_data = GPT21024Dataset(args.root_dir,args.ids_file,mode='train',length=3000) # valid_data = GPT21024Dataset(args.root_dir,args.ids_file,mode='valid',length=500) test_data = GPT21024Dataset(args.root_dir,args.ids_file,mode='test',length=500)# model_file and config_file are files used to load finetuned model, change these name as per your file names# model_file = os.path.join(args.model_dir, 'model_data{}_trained_after_{}_epochs_only_sum_loss_ignr_pad.bin'.format(len(train_data),args.num_train_epochs)) # config_file = os.path.join(args.model_dir, 'config_data{}_trained_after_{}_epochs_only_sum_loss_ignr_pad.json'.format(len(train_data),args.num_train_epochs))# path to model and config files model_file = "345-model_O0_data3000_trained_after_5_epochs_only_sum_loss_ignr_pad.bin" config_file = "345-config_O0_data3000_trained_after_5_epochs_only_sum_loss_ignr_pad.json"config = GPT2Config.from_json_file(config_file) model = GPT2LMHeadModel(config) state_dict = torch.load(model_file) model.load_state_dict(state_dict) model.eval() model.to(args.device)generate_sample(test_data, tokenizer, model, num=2, length=100, temperature=1, top_k=10, top_p=0.5, device=args.device)

生成結果：

HBox(children=(IntProgress(value=0), HTML(value=''))) new_articleRome -LRB- CNN -RRB- -- A cruise ship of the Costa Cruises line is adrift off the coast of the Seychelles after a fire in its engine room, the Italian coast guard said Monday. The ship, the Allegra, is a sister of the Costa Concordia, which wrecked off the coast of Italy on January 13, killing at least 21 people. The fire left the Allegra without propulsion, although its communications equipment is intact, the authorities said. The Allegra's fire has been put out, and the passengers are all in good health, the authorities said. The Seychelles is sending a tug, and merchant ships in the area are steaming toward the Allegra, the coast guard said.generated_summaryThe ship is carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carrying cargo from the Seychelles . The ship was carryingactual_summaryAn engine room fire leaves the Costa Allegra without propulsion, authorities say. Its sister ship, the Costa Concordia, shipwrecked last month, killing at least 21. <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|> <|pad|>

代碼鏈接：https://github.com/AigcLwq/miniChatgpt.git

T5

下次迭代更新

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