Delete generate.py

generate.py

@@ -1,187 +0,0 @@
-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 smp_generate(pre_str):
-
-    from tokenizations import tokenization_bert
-
-    os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2,3'  # 此处设置程序使用哪些显卡
-    length = 500
-    batch_size = 1
-    nsamples = 1
-    temperature = 1
-    topk = 8
-    topp = 0
-    repetition_penalty = 1.0
-    model_path = 'pretrained'
-    tokenizer_path = 'cache/vocab.txt'
-    save_samples = False
-    save_samples_path = '.'
-    fast_pattern = True
-    prefix = pre_str
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    tokenizer = tokenization_bert.BertTokenizer(vocab_file=tokenizer_path)
-    model = GPT2LMHeadModel.from_pretrained(model_path)
-    model.to(device)
-    model.eval()
-
-    n_ctx = model.config.n_ctx
-
-    if length == -1:
-        length = model.config.n_ctx
-
-    while True:
-        raw_text = 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=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"
-                text = ''.join(text).replace('##', '').strip()
-                return text
-
-
-if __name__ == '__main__':
-    print(smp_generate('曹贼休走'))
-