def __init__(self, model_path=None, model_config=None):
self.model = None
if (model_path is not None and model_config is not None):
self.load_model(model_path, model_config)
else:
self.load_model()
self.tokenizer = tokenization_bert.BertTokenizer(
vocab_file='cache/vocab_with_title.txt')
self.checker = Checker()
with open('./cache/label_to_id.json', 'r', encoding='utf-8') as f:
self.title_to_ids = json.load(f)
def get_prob(context, topk, genre, title):
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = tokenization_bert.BertTokenizer(vocab_file='cache/vocab_fine_tuning.txt')
model_config = pytorch_pretrained_bert.GPT2Config.from_json_file('cache/model_config_single.json')
model_state_dict = torch.load('cache/model_single/model_epoch_1.pt')
model = GPT2LMHeadModel(config=model_config)
model.load_state_dict(model_state_dict)
model.to(device)
model.eval()
batch_size = 1
temperature = 1
context_tokens = []
with open('./cache/label_to_id.json','r',encoding='utf-8') as f:
title_to_ids = json.load(f)
try:
ids = title_to_ids[genre]
context_tokens.append(ids)
except:
ids = title_to_ids['七言律诗']
context_tokens.append(ids)
context_tokens.append(100)
context_tokens.extend(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(title)))
context_tokens.append(4282) # 4282 is #
raw_text = context
if raw_text != "":
context_tokens.extend(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(raw_text)))
watcher = WatchProb(model=model, context=context_tokens, tokenizer=tokenizer, temperature=temperature, top_k=topk, device=device)
prob_dis = watcher.show_prob(topk=topk)
eight_cumu = watcher.show_cumulative(0.8)
nine_cumu = watcher.show_cumulative(0.9)
ninefive_cumu = watcher.show_cumulative(0.95)
prob_dis.append("")
prob_dis.append("")
prob_dis.append("0.8累计覆盖: "+str(eight_cumu))
prob_dis.append("0.9累计覆盖: "+str(nine_cumu))
prob_dis.append("0.95累计覆盖: "+str(ninefive_cumu))
return prob_dis
def main():
length = -1
batch_size = 1
nsamples = 18
temperature = 1
topk = 5
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = tokenization_bert.BertTokenizer(
vocab_file='cache/vocab_small.txt')
model_config = pytorch_transformers.GPT2Config.from_json_file(
'config/model_config_small.json')
model = GPT2LMHeadModel(
config=model_config).from_pretrained('model/final_model')
model.to(device)
model.eval()
if length == -1:
length = model.config.n_ctx // 2
elif length > model.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
model.config.n_ctx)
while True:
raw_text = '萧炎'
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(raw_text))
generated = 0
for _ in range(nsamples // batch_size):
out = sample_sequence(model=model,
length=length,
context=context_tokens,
start_token=None,
batch_size=batch_size,
temperature=temperature,
top_k=topk,
device=device)
out = out[:, len(context_tokens):].tolist()
for i in range(batch_size):
generated += 1
text = tokenizer.convert_ids_to_tokens(out[i])
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(''.join(text))
print("=" * 80)
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')
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=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('--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('--no_wordpiece',
action='store_true',
help='不做word piece切词')
args = parser.parse_args()
print('args:\n' + args.__repr__())
if args.no_wordpiece:
import tokenization_bert_without_wordpiece as tokenization_bert
else:
import tokenization_bert
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file(
args.model_config)
print('config:\n' + model_config.to_json_string())
n_ctx = model_config.n_ctx
full_tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.tokenizer_path)
full_tokenizer.max_len = n_ctx
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
output_dir = args.output_dir
if raw:
print('building files')
build_files(raw_data_path=raw_data_path,
tokenized_data_path=tokenized_data_path,
full_tokenizer=full_tokenizer,
num_pieces=num_pieces)
print('files built')
if not args.pretrained_model:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=model_config)
else:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(
args.pretrained_model)
model.train()
model.to(device)
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 = pytorch_transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = pytorch_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)
multi_gpu = True
print('starting training')
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:
running_loss = 0
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
random.shuffle(samples)
for step in range(len(samples) // batch_size):
# prepare data
batch = samples[step * batch_size:(step + 1) * batch_size]
batch_labels = []
batch_inputs = []
for ids in batch:
int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
batch_labels = torch.tensor(batch_labels).long().to(device)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
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 (step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
if (step + 1) % log_step == 0:
print(
'now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'
.format(
datetime.now().hour,
datetime.now().minute,
(step + 1) // gradient_accumulation, piece_num,
epoch + 1,
running_loss * gradient_accumulation / log_step))
running_loss = 0
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')
import pytorch_transformers
import torch
import os
import json
import random
import tokenization_bert
import numpy as np
from datetime import datetime
from tqdm import tqdm
from torch.nn import DataParallel
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" # 此处设置程序使用哪些显卡
model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file(
'config/model_config_small.json')
n_ctx = model_config.n_ctx
full_tokenizer = tokenization_bert.BertTokenizer(
vocab_file='cache/vocab_small.txt')
full_tokenizer.max_len = n_ctx
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('using device:', device)
raw_data_path = 'data/train.json'
tokenized_data_path = 'data/tokenized/'
raw = True # 选择是否从零开始构建数据集
epochs = 5
batch_size = 12
lr = 1.5e-4
warmup_steps = 2000
log_step = 1
stride = 768
gradient_accumulation = 1
fp16 = False # 不支持半精度的显卡请勿打开
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
default='0,1,2,3',
type=str,
required=False,
help='生成设备')
parser.add_argument('--length',
default=-1,
type=int,
required=False,
help='生成长度')
parser.add_argument('--batch_size',
default=1,
type=int,
required=False,
help='生成的batch size')
parser.add_argument('--nsamples',
default=10,
type=int,
required=False,
help='生成几个样本')
parser.add_argument('--temperature',
default=1,
type=float,
required=False,
help='生成温度')
parser.add_argument('--topk',
default=8,
type=int,
required=False,
help='最高几选一')
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_small.json',
type=str,
required=False,
help='模型参数')
parser.add_argument('--tokenizer_path',
default='cache/vocab_small.txt',
type=str,
required=False,
help='词表路径')
parser.add_argument('--model_path',
default='model/final_model',
type=str,
required=False,
help='模型路径')
parser.add_argument('--prefix',
default='萧炎',
type=str,
required=False,
help='生成文章的开头')
args = parser.parse_args()
print(args)
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
length = args.length
batch_size = args.batch_size
nsamples = args.nsamples
temperature = args.temperature
topk = args.topk
topp = args.topp
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
model = GPT2LMHeadModel.from_pretrained(args.model_path)
model.to(device)
model.eval()
if length == -1:
length = model.config.n_ctx // 2
elif length > model.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
model.config.n_ctx)
while True:
raw_text = args.prefix
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(raw_text))
generated = 0
for _ in range(nsamples // batch_size):
out = sample_sequence(model=model,
length=length,
context=context_tokens,
temperature=temperature,
top_k=topk,
top_p=topp,
device=device)
out = out.tolist()
for i in range(batch_size):
generated += 1
text = tokenizer.convert_ids_to_tokens(out[0])
for i, item in enumerate(text[:-1]): # 确保英文前后有空格
if is_word(item) and is_word(text[i + 1]):
text[i] = item + ' '
for i, item in enumerate(text):
if item == '[MASK]':
text[i] = ''
if item == '[CLS]' or item == '[SEP]':
text[i] = '\n'
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
text = ''.join(text).replace('##', '').strip()
print(text)
print("=" * 80)
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('--length',
default=-1,
type=int,
required=False,
help='生成长度')
parser.add_argument('--temperature',
default=1,
type=float,
required=False,
help='生成温度，越高越随机')
parser.add_argument('--topk',
default=8,
type=int,
required=False,
help='生成的时候最高几选一')
parser.add_argument('--topp',
default=0,
type=float,
required=False,
help='生成的时候积累概率最高多少')
parser.add_argument('--model_config',
default='config/model_config_small.json',
type=str,
required=False,
help='模型参数路径')
parser.add_argument('--tokenizer_path',
default='cache/vocab_small.txt',
type=str,
required=False,
help='词表路径')
parser.add_argument('--model_path',
default='model/final_model',
type=str,
required=False,
help='模型路径')
parser.add_argument('--save_path',
default='generated/',
type=str,
required=False,
help='存放生成的文件的路径')
parser.add_argument('--articles_per_title',
default=5,
type=int,
required=False,
help='每个标题生成多少篇文章')
parser.add_argument('--titles',
default='萧炎',
type=str,
required=False,
help='标题列表，是一个字符串，用空格分开')
parser.add_argument('--titles_file',
default='',
type=str,
required=False,
help='标题列表文件，文件中每行一个标题。如果这个选项有值则titles无效')
args = parser.parse_args()
print(args)
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
length = args.length
temperature = args.temperature
topk = args.topk
topp = args.topp
titles = args.title.split() # 列表，里面每个元素是一个生成的标题
if args.titles_file:
with open(args.titles_file, 'r') as f:
titles = [line.strip('\n') for line in f.readlines()]
articles_per_title = args.articles_per_title # 这里定义一个标题生成多少篇文章
save_path = args.save_path # 设置存到哪
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
model_config = pytorch_transformers.GPT2Config.from_json_file(
args.model_config)
model = GPT2LMHeadModel.from_pretrained(args.model_path)
model.to(device)
model.eval()
if not os.path.exists(save_path):
os.mkdir(save_path)
if length == -1:
length = model.config.n_ctx // 2
elif length > model.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
model.config.n_ctx)
for i, title in enumerate(titles):
for j in range(articles_per_title):
with open(save_path + str(i * j), 'w') as f:
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(title))
generated = 0
out = sample_sequence(model=model,
length=length,
context=context_tokens,
temperature=temperature,
top_k=topk,
top_p=topp,
device=device)
out = out.tolist()
generated += 1
text = tokenizer.convert_ids_to_tokens(out[0])
for i, item in enumerate(text[:-1]): # 确保英文前后有空格
if is_word(item) and is_word(text[i + 1]):
text[i] = item + ' '
for i, item in enumerate(text):
if item == '[MASK]':
text[i] = ''
if item == '[CLS]' or item == '[SEP]':
text[i] = '\n'
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
text = ''.join(text).replace('##', '').strip()
# text = ''.join(text.split('\n')[:-1])
print(text)
f.write(text)
print("=" * 80)
