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interact.py
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interact.py
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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 import GPT2TokenizerFast, GPT2LMHeadModel, GPT2Config
from transformers import BertTokenizerFast
# from transformers import BertTokenizer
from os.path import join, exists
from itertools import zip_longest, chain
# from chatbot.model import DialogueGPT2Model
from dataset import MyDataset
from torch.utils.data import Dataset, DataLoader
from torch.nn import CrossEntropyLoss
from sklearn.model_selection import train_test_split
import torch.nn.functional as F
from util import create_logger
PAD = '[PAD]'
pad_id = 0
def set_args():
"""
Sets up the arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='0', 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=0.8, 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/interact.log', type=str, required=False, help='interact日志存放位置')
parser.add_argument('--vocab_path', default='vocab/vocab.txt', type=str, required=False, help='选择词库')
parser.add_argument('--model_path', default='model/epoch40', type=str, required=False, help='对话模型路径')
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=3, 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')
return parser.parse_args()
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 (vocab 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 clear
top_k = min(top_k, logits.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
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] # 用top_k中最小的卡出需要移除的
logits[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) # 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 # 有时top_p 把所有的都token都过滤掉了,所以这里强行保留第一个。
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
return logits
def main():
args = set_args()
logger = create_logger(args.log_path)
# 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 = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
# tokenizer = BertTokenizer(vocab_file=args.voca_path)
model = GPT2LMHeadModel.from_pretrained(args.model_path)
model = model.to(device)
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 + '/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:")
# text = "你好"
if args.save_samples_path:
samples_file.write("user:{}\n".format(text))
text_ids = tokenizer.encode(text, add_special_tokens=False)
history.append(text_ids)
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:]):# 获取时间上最近的历史
input_ids.extend(history_utr) # 把最近的user问话,拼接进去。
input_ids.append(tokenizer.sep_token_id) # 添加分割[SEP]
input_ids = torch.tensor(input_ids).long().to(device)
input_ids = input_ids.unsqueeze(0)
response = [] # 根据context,生成的response
# 最多生成max_len个token
for _ in range(args.max_len):
outputs = model.forward(input_ids=input_ids)
logits = outputs.logits # 没有进行softmax输出的结果。
next_token_logits = logits[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 id in set(response):
next_token_logits[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
next_token_logits[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) # 避免重复出现,按概率 无放回采样
if next_token == tokenizer.sep_token_id: # 遇到[SEP]则表明response生成结束
break
response.append(next_token.item())
input_ids = torch.cat((input_ids, next_token.unsqueeze(0)), dim=1)
# his_text = tokenizer.convert_ids_to_tokens(curr_input_tensor.tolist())
# print("his_text:{}".format(his_text))
history.append(response)
text = tokenizer.convert_ids_to_tokens(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__':
# (由于闲聊对话生成的内容长度不是很长,因此生成部分在CPU上跑速度也挺快的)
# python interact.py --no_cuda --model_path model_epoch40_50w --max_history_len 3
main()