import torch
import torch.nn.functional as F
import os
import argparse
from tqdm import trange
from transformers import GPT2LMHeadModel
def is_word(word):
for item in list(word):
if item not in 'qwertyuiopasdfghjklzxcvbnm':
return False
return True
def _is_chinese_char(char):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
cp = ord(char)
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
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)
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
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if 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 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
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
return logits
def sample_sequence(model, context, length, n_ctx, tokenizer, temperature=1.0, top_k=30, top_p=0.0, repitition_penalty=1.0,
device='cpu'):
context = torch.tensor(context, dtype=torch.long, device=device)
context = context.unsqueeze(0)
generated = context
with torch.no_grad():
for _ in trange(length):
inputs = {'input_ids': generated[0][-(n_ctx - 1):].unsqueeze(0)}
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, :]
for id in set(generated):
next_token_logits[id] /= repitition_penalty
next_token_logits = next_token_logits / temperature
next_token_logits[tokenizer.convert_tokens_to_ids('[UNK]')] = -float('Inf')
filtered_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 generated.tolist()[0]
def fast_sample_sequence(model, context, length, temperature=1.0, top_k=30, top_p=0.0, device='cpu'):
inputs = torch.LongTensor(context).view(1, -1).to(device)
if len(context) > 1:
_, past = model(inputs[:, :-1], None)[:2]
prev = inputs[:, -1].view(1, -1)
else:
past = None
prev = inputs
generate = [] + context
with torch.no_grad():
for i in trange(length):
output = model(prev, past=past)
output, past = output[:2]
output = output[-1].squeeze(0) / temperature
filtered_logits = top_k_top_p_filtering(output, top_k=top_k, top_p=top_p)
next_token = torch.multinomial(torch.softmax(filtered_logits, dim=-1), num_samples=1)
generate.append(next_token.item())
prev = next_token.view(1, 1)
return generate
# 通过命令行参数--fast_pattern，指定模式
def generate(n_ctx, model, context, length, tokenizer, temperature=1, top_k=0, top_p=0.0, repitition_penalty=1.0, device='cpu',
is_fast_pattern=False):
if is_fast_pattern:
return fast_sample_sequence(model, context, length, temperature=temperature, top_k=top_k, top_p=top_p,
device=device)
else:
return sample_sequence(model, context, length, n_ctx, tokenizer=tokenizer, temperature=temperature, top_k=top_k, top_p=top_p,
repitition_penalty=repitition_penalty, device=device)
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')
parser.add_argument('--no_wordpiece', action='store_true', help='不做word piece切词')
parser.add_argument('--segment', action='store_true', help='中文以词为单位')
parser.add_argument('--fast_pattern', action='store_true', help='采用更加快的方式生成文本')
parser.add_argument('--save_samples', action='store_true', help='保存产生的样本')
parser.add_argument('--save_samples_path', default='.', type=str, required=False, help="保存样本的路径")
parser.add_argument('--repetition_penalty', default=1.0, type=float, required=False)
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
length = args.length
batch_size = args.batch_size
nsamples = args.nsamples
temperature = args.temperature
topk = args.topk
topp = args.topp
repetition_penalty = args.repetition_penalty
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()
n_ctx = model.config.n_ctx
if length == -1:
length = model.config.n_ctx
if args.save_samples:
if not os.path.exists(args.save_samples_path):
os.makedirs(args.save_samples_path)
samples_file = open(args.save_samples_path + '/samples.txt', 'w', encoding='utf8')
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 = generate(
n_ctx=n_ctx,
model=model,
context=context_tokens,
length=length,
is_fast_pattern=args.fast_pattern, tokenizer=tokenizer,
temperature=temperature, top_k=topk, top_p=topp, repitition_penalty=repetition_penalty, device=device
)
for i in range(batch_size):
generated += 1
text = tokenizer.convert_ids_to_tokens(out)
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] = ''
elif item == '[CLS]':
text[i] = '\n\n'
elif item == '[SEP]':
text[i] = '\n'
info = "=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40 + "\n"
print(info)
text = ''.join(text).replace('##', '').strip()
print(text)
if args.save_samples:
samples_file.write(info)
samples_file.write(text)
samples_file.write('\n')
samples_file.write('=' * 90)
samples_file.write('\n' * 2)
print("=" * 80)
if generated == nsamples:
# close file when finish writing.
if args.save_samples:
samples_file.close()
break
if __name__ == '__main__':
main()
