Update generate.py

generate.py

@@ -1,222 +1,30 @@
-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='./model_config_small.json', type=str, required=False,
- help='模型参数')
- parser.add_argument('--tokenizer_path', default='./vocab_small.txt', type=str, required=False, help='词表路径')
- parser.add_argument('--model_path', default='./', 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()
+import gradio as gr
+import subprocess
+
+
+def generate_text(length, prefix, temperature,topk,topp,rep):
+ # 构建命令行参数
+ my_prefix = "--prefix=" + prefix + ","
+ args = ["python", "generate.py", f"--length={int(length)}", f"--nsamples=1", f"--prefix={prefix}", f"--temperature={temperature}",f"--batch_size=1",f"--topk={int(topk)}",f"--topp={topp}",f"--repetition_penalty={rep}","--fast_pattern","--tokenizer_path=./vocab.txt","--model_config=./config.json"]
+ 
+ # 执行命令并获取输出
+ process = subprocess.Popen(args, stdout=subprocess.PIPE)
+ output, error = process.communicate()
+ output = output.decode("utf-8")
+ 
+ return output
+
+input_length = gr.Slider(label="生成文本长度", minimum=10, maximum=500, value=500,step=10)
+input_prefix = gr.Textbox(label="起始文本")
+input_temperature = gr.Slider(label="生成温度", minimum=0, maximum=2, value=1, step=0.01)
+#input_batchsize = gr.Slider(label="生成的batch size", minimum=1, maximum=1, value=1,step=1)
+input_topk = gr.Slider(label="最高几选一", minimum=1, maximum=48, value=32, step=1)
+input_topp = gr.Slider(label="最高积累概率", minimum=0, maximum=1, value=0,step=0.01)
+input_repeat_penality = gr.Slider(label="重复罚值", minimum=0, maximum=15, value=10,step=0.01)
+
+output_text = gr.Textbox(label="生成的文本")
+
+title = "GPT2中文文本生成器"
+description = "cpu推理约1s/字，温度太低基本是无意义字符"
+
+gr.Interface(fn=generate_text, inputs=[input_length, input_prefix, input_temperature,input_topk,input_topp,input_repeat_penality], outputs=output_text, title=title, description=description).launch()