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 import logging from transformers.modeling_gpt2 import GPT2Config, GPT2LMHeadModel from transformers import BertTokenizer from os.path import join, exists from itertools import zip_longest, chain from dataset import MyDataset from torch.utils.data import Dataset, DataLoader from torch.nn import CrossEntropyLoss from sklearn.model_selection import train_test_split from train import create_model import torch.nn.functional as F import copy PAD = '[PAD]' pad_id = 0 def set_interact_args(): """ Sets up the training arguments. """ parser = argparse.ArgumentParser() parser.add_argument('--device', default='1', type=str, required=False, help='生成设备') parser.add_argument('--temperature', default=1, type=float, required=False, help='生成的temperature') parser.add_argument('--topk', default=8, type=int, required=False, help='最高k选1') parser.add_argument('--topp', default=0, type=float, required=False, help='最高积累概率') parser.add_argument('--model_config', default='config/model_config_dialogue_small.json', type=str, required=False, help='模型参数') parser.add_argument('--log_path', default='data/interacting_mmi.log', type=str, required=False, help='interact_mmi日志存放位置') parser.add_argument('--voca_path', default='vocabulary/vocab_small.txt', type=str, required=False, help='选择词库') parser.add_argument('--dialogue_model_path', default='dialogue_model/', type=str, required=False, help='dialogue_model路径') parser.add_argument('--mmi_model_path', default='mmi_model/', type=str, required=False, help='互信息mmi_model路径') parser.add_argument('--save_samples_path', default="sample/", type=str, required=False, help="保存聊天记录的文件路径") parser.add_argument('--repetition_penalty', default=1.0, type=float, required=False, help="重复惩罚参数，若生成的对话重复性较高，可适当提高该参数") parser.add_argument('--seed', type=int, default=None, help='设置种子用于生成随机数，以使得训练的结果是确定的') parser.add_argument('--max_len', type=int, default=25, help='每个utterance的最大长度,超过指定长度则进行截断') parser.add_argument('--max_history_len', type=int, default=5, help="dialogue history的最大长度") parser.add_argument('--no_cuda', action='store_true', help='不使用GPU进行预测') parser.add_argument('--batch_size', type=int, default=5, help='批量生成response，然后经过MMI模型进行筛选') parser.add_argument('--debug', action='store_true', help='指定该参数，可以查看生成的所有候选的reponse，及其loss') return parser.parse_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') # 创建一个handler，用于写入日志文件 file_handler = logging.FileHandler( filename=args.log_path) file_handler.setFormatter(formatter) file_handler.setLevel(logging.INFO) logger.addHandler(file_handler) # 创建一个handler，用于将日志输出到控制台 console = logging.StreamHandler() console.setLevel(logging.DEBUG) console.setFormatter(formatter) logger.addHandler(console) return logger 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) filtering Args: 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) """ assert logits.dim() == 2 top_k = min(top_k, logits[0].size(-1)) # Safety check if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k # torch.topk()返回最后一维最大的top_k个元素，返回值为二维(values,indices) # ...表示其他维度由计算机自行推断 for logit in logits: indices_to_remove = logit < torch.topk(logit, top_k)[0][..., -1, None] logit[indices_to_remove] = filter_value # 对于topk之外的其他元素的logits值设为负无穷 if top_p > 0.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) # 对logits进行递减排序 cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 for index, logit in enumerate(logits): indices_to_remove = sorted_indices[index][sorted_indices_to_remove[index]] logit[indices_to_remove] = filter_value return logits def main(): args = set_interact_args() logger = create_logger(args) # 当用户使用GPU,并且GPU可用时 args.cuda = torch.cuda.is_available() and not args.no_cuda device = 'cuda' if args.cuda else 'cpu' logger.info('using device:{}'.format(device)) os.environ["CUDA_VISIBLE_DEVICES"] = args.device tokenizer = BertTokenizer(vocab_file=args.voca_path) # 对话model dialogue_model = GPT2LMHeadModel.from_pretrained(args.dialogue_model_path) dialogue_model.to(device) dialogue_model.eval() # 互信息mmi model mmi_model = GPT2LMHeadModel.from_pretrained(args.mmi_model_path) mmi_model.to(device) mmi_model.eval() if args.save_samples_path: if not os.path.exists(args.save_samples_path): os.makedirs(args.save_samples_path) samples_file = open(args.save_samples_path + '/mmi_samples.txt', 'a', encoding='utf8') samples_file.write("聊天记录{}:\n".format(datetime.now())) # 存储聊天记录，每个utterance以token的id的形式进行存储 history = [] print('开始和chatbot聊天，输入CTRL + Z以退出') while True: try: text = input("user:") if args.save_samples_path: samples_file.write("user:{}\n".format(text)) history.append(tokenizer.encode(text)) input_ids = [tokenizer.cls_token_id] # 每个input以[CLS]为开头 for history_id, history_utr in enumerate(history[-args.max_history_len:]): input_ids.extend(history_utr) input_ids.append(tokenizer.sep_token_id) # 用于批量生成response，维度为(batch_size,token_len) input_ids = [copy.deepcopy(input_ids) for _ in range(args.batch_size)] curr_input_tensors = torch.tensor(input_ids).long().to(device) generated = [] # 二维数组，维度为(生成的response的最大长度，batch_size)，generated[i,j]表示第j个response的第i个token的id finish_set = set() # 标记是否所有response均已生成结束，若第i个response生成结束，即生成了sep_token_id，则将i放入finish_set # 最多生成max_len个token for _ in range(args.max_len): outputs = dialogue_model(input_ids=curr_input_tensors) # print ("outputs",outputs) next_token_logits = outputs[0][:, -1, :] # 对于已生成的结果generated中的每个token添加一个重复惩罚项，降低其生成概率 for index in range(args.batch_size): for token_id in set([token_ids[index] for token_ids in generated]): next_token_logits[index][token_id] /= args.repetition_penalty next_token_logits = next_token_logits / args.temperature # 对于[UNK]的概率设为无穷小，也就是说模型的预测结果不可能是[UNK]这个token for next_token_logit in next_token_logits: next_token_logit[tokenizer.convert_tokens_to_ids('[UNK]')] = -float('Inf') filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=args.topk, top_p=args.topp) # torch.multinomial表示从候选集合中无放回地进行抽取num_samples个元素，权重越高，抽到的几率越高，返回元素的下标 next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1) # 判断是否有response生成了[SEP],将已生成了[SEP]的resposne进行标记 for index, token_id in enumerate(next_token[:, 0]): if token_id == tokenizer.sep_token_id: finish_set.add(index) # 检验是否所有的response均已生成[SEP] finish_flag = True # 是否所有的response均已生成[SEP]的token for index in range(args.batch_size): if index not in finish_set: # response批量生成未完成 finish_flag = False break if finish_flag: break generated.append([token.item() for token in next_token[:, 0]]) # 将新生成的token与原来的token进行拼接 curr_input_tensors = torch.cat((curr_input_tensors, next_token), dim=-1) candidate_responses = [] # 生成的所有候选response for batch_index in range(args.batch_size): response = [] for token_index in range(len(generated)): if generated[token_index][batch_index] != tokenizer.sep_token_id: response.append(generated[token_index][batch_index]) else: break candidate_responses.append(response) # mmi模型的输入 if args.debug: print("candidate response:") samples_file.write("candidate response:\n") min_loss = float('Inf') best_response = "" for response in candidate_responses: mmi_input_id = [tokenizer.cls_token_id] # 每个input以[CLS]为开头 mmi_input_id.extend(response) mmi_input_id.append(tokenizer.sep_token_id) for history_utr in reversed(history[-args.max_history_len:]): mmi_input_id.extend(history_utr) mmi_input_id.append(tokenizer.sep_token_id) mmi_input_tensor = torch.tensor(mmi_input_id).long().to(device) out = mmi_model(input_ids=mmi_input_tensor, labels=mmi_input_tensor) loss = out[0].item() if args.debug: text = tokenizer.convert_ids_to_tokens(response) print("{} loss:{}".format("".join(text), loss)) samples_file.write("{} loss:{}\n".format("".join(text), loss)) if loss < min_loss: best_response = response min_loss = loss history.append(best_response) text = tokenizer.convert_ids_to_tokens(best_response) print("chatbot:" + "".join(text)) if args.save_samples_path: samples_file.write("chatbot:{}\n".format("".join(text))) except KeyboardInterrupt: if args.save_samples_path: samples_file.close() break if __name__ == '__main__': main()