iniit

app.py

@@ -0,0 +1,65 @@
+"""
+This script summarizes from a paragraph(normally 512 length).
+"""
+
+import torch
+import gradio as gr
+
+from transformers import PegasusForConditionalGeneration, Text2TextGenerationPipeline
+from article_extractor.tokenizers_pegasus import PegasusTokenizer
+
+
+class SummaryExtractor(object):
+    def __init__(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = PegasusForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese').to(self.device)
+        self.tokenizer = PegasusTokenizer.from_pretrained("IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese")
+
+
+        self.text2text_genr = Text2TextGenerationPipeline(self.model, self.tokenizer, device=self.device)
+
+    def extract(self, content: str, min=20, max=30) -> str:
+        # inputs = self.tokenizer(content, max_length=512, return_tensors='pt').to(device=self.device)
+        # summary_ids = self.model.generate(inputs['input_ids'])
+        # return self.tokenizer.batch_decode(summary_ids,
+        #                                       skip_special_tokens=True,
+        #                                       clean_up_tokenization_spaces=False)[0]
+        return str(self.text2text_genr(content, do_sample=False, min_length=min, max_length=max, num_return_sequences=3)[0]["generated_text"])
+
+
+t_randeng = SummaryExtractor()
+
+
+def randeng_extract(content):
+    return t_randeng.extract(content)
+
+def similarity_check(query: str, doc: str):
+    return "similarity result"
+
+with gr.Blocks() as app:
+    gr.Markdown("从下面的标签选择不同的摘要模型, 在左侧输入原文")
+    # with gr.Tab("CamelBell-Chinese-LoRA"):
+    #     text_input = gr.Textbox()
+    #     text_output = gr.Textbox()
+    #     text_button = gr.Button("生成摘要")
+    with gr.Tab("Randeng-Pegasus-523M"):
+        text_input_1 = gr.Textbox()
+        text_output_1 = gr.Textbox()
+        text_button_1 = gr.Button("生成摘要")
+    # with gr.Tab("Flip Image"):
+    #     with gr.Row():
+    #         image_input = gr.Image()
+    #         image_output = gr.Image()
+    #     image_button = gr.Button("Flip")
+    with gr.Tab("相似度检测"):
+        with gr.Row():
+            text_input_query = gr.Textbox()
+            text_input_doc = gr.Textbox()
+        text_button_similarity = gr.Button("对比相似度")
+        text_output_similarity = gr.Textbox()
+
+    # text_button.click(tuoling_extract, inputs=text_input, outputs=text_output)
+    text_button_1.click(randeng_extract, inputs=text_input_1, outputs=text_output_1)
+    text_button_similarity.click(similarity_check, inputs=text_input_query, outputs=text_input_doc)
+
+app.launch()

article_extractor/tokenizers_pegasus.py

@@ -0,0 +1,602 @@
+import sys
+sys.path.append('../')
+from article_extractor.data_utils import (
+    _is_control,
+    _is_punctuation,
+    _is_whitespace,
+    _is_chinese_char)
+from transformers import PreTrainedTokenizer
+from transformers import logging
+from typing import List, Optional, Tuple, Union
+import collections
+import os
+import unicodedata
+import re
+import jieba
+import sys
+
+# 提取摘要逻辑实现
+
+# sys.path.append("../../../../")
+
+jieba.dt.tmp_dir = os.path.expanduser(
+    "../tmp/")
+# jieba.enable_parallel(8)
+jieba.initialize()
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        tokens = reader.readlines()
+    for index, token in enumerate(tokens):
+        token = token.rstrip("\n")
+        vocab[token] = index
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class PegasusTokenizer(PreTrainedTokenizer):
+    # copy from BertTokenizer
+    r"""
+    Construct a Pegasus tokenizer. Based on WordPiece.
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+    Args:
+        vocab_file (`str`):
+            File containing the vocabulary.
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+            Whether or not to do basic tokenization before WordPiece.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    #     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    #     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    #     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(self,
+                 vocab_file,
+                 do_lower_case=True,
+                 do_basic_tokenize=True,
+                 never_split=None,
+                 pad_token="<pad>",
+                 eos_token="</s>",
+                 unk_token="<unk>",
+                 mask_token="<mask_2>",
+                 mask_token_sent="<mask_1>",
+                 additional_special_tokens=None,
+                 sep_token="[SEP]",
+                 cls_token="[CLS]",
+                 tokenize_chinese_chars=True,
+                 strip_accents=None,
+                 offset=100,
+                 pre_tokenizer=lambda x: jieba.cut(x, HMM=False),
+                 **kwargs):
+        self.offset = offset
+
+        if additional_special_tokens is not None:
+            if not isinstance(additional_special_tokens, list):
+                raise TypeError(
+                    f"additional_special_tokens should be of type {type(list)}, \
+                     but is {type(additional_special_tokens)}"
+                )
+
+            additional_special_tokens_extended = (
+                ([mask_token_sent] + additional_special_tokens)
+                if mask_token_sent not in additional_special_tokens
+                and mask_token_sent is not None else additional_special_tokens)
+
+            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
+            additional_special_tokens_extended += [
+                f"<unk_{i}>" for i in range(
+                    len(additional_special_tokens_extended), self.offset - 1)
+            ]
+
+            if len(set(additional_special_tokens_extended)) != len(
+                    additional_special_tokens_extended):
+                raise ValueError(
+                    f"Please make sure that the provided additional_special_tokens \
+                        do not contain an incorrectly shifted list of <unk_x> tokens. \
+                        Found {additional_special_tokens_extended}."
+                )
+            additional_special_tokens = additional_special_tokens_extended
+        else:
+            additional_special_tokens = [
+                mask_token_sent
+            ] if mask_token_sent is not None else []
+            # additional_special_tokens += [f"<unk_{i}>" for i in range(3, self.offset)]
+
+        # print("additional_special_tokens: ", additional_special_tokens)
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. \
+                To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+
+        super().__init__(
+            do_lower_case=do_lower_case,
+            do_basic_tokenize=do_basic_tokenize,
+            never_split=never_split,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            eos_token=eos_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            additional_special_tokens=additional_special_tokens,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        self.pre_tokenizer = pre_tokenizer
+        self.mask_token_sent = mask_token_sent
+        self.vocab = load_vocab(vocab_file)
+
+        self.vocab[self.eos_token] = self.vocab.pop("[unused1]")
+        # self.vocab[self.eos_token] = self.vocab.pop("[unused2]")
+        self.vocab[self.pad_token] = self.vocab.pop("[PAD]")
+        self.vocab[self.unk_token] = self.vocab.pop("[UNK]")
+
+        if self.mask_token_sent is not None:
+            self.vocab[self.mask_token] = self.vocab.pop("[unused3]")
+            self.vocab[self.mask_token_sent] = self.vocab.pop("[unused2]")
+
+        self.ids_to_tokens = collections.OrderedDict([
+            (ids, tok) for tok, ids in self.vocab.items()
+        ])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+            self.basic_tokenizer = BasicTokenizer(
+                do_lower_case=do_lower_case,
+                never_split=never_split,
+                tokenize_chinese_chars=tokenize_chinese_chars,
+                strip_accents=strip_accents,
+            )
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
+                                                      unk_token=self.unk_token)
+
+    @property
+    def do_lower_case(self):
+        return self.basic_tokenizer.do_lower_case
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text):
+        split_tokens = []
+        # print("pegasus_tokenizer: ", text)
+        for text in self.pre_tokenizer(text):
+            if text in self.vocab:
+                split_tokens.append(text)
+            else:
+                if self.do_basic_tokenize:
+                    for token in self.basic_tokenizer.tokenize(
+                            text, never_split=self.all_special_tokens):
+
+                        # If the token is part of the never_split set
+                        if token in self.basic_tokenizer.never_split:
+                            split_tokens.append(token)
+                        else:
+                            split_tokens += self.wordpiece_tokenizer.tokenize(
+                                token)
+                else:
+                    split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    @staticmethod
+    def _cjk_punctuation():
+        return u'\uff02\uff03\uff04\uff05\uff06\uff07\uff08\uff09\uff0a\uff0b\uff0c\uff0d\uff0f\uff1a\uff1b\uff1c\uff1d\
+            \uff1e\uff20\uff3b\uff3c\uff3d\uff3e\uff3f\uff40\uff5b\uff5c\uff5d\uff5e\uff5f\uff60\uff62\
+            \uff63\uff64\u3000\u3001\u3003\u3008\u3009\u300a\u300b\u300c\u300d\u300e\u300f\u3010\u3011\u3014\
+            \u3015\u3016\u3017\u3018\u3019\u301a\u301b\u301c\u301d\u301e\u301f\u3030\u303e\u303f\u2013\u2014\
+            \u2018\u2019\u201b\u201c\u201d\u201e\u201f\u2026\u2027\ufe4f\ufe51\ufe54\u00b7\uff01\uff1f\uff61\u3002'
+
+    def convert_ids_to_tokens(
+            self,
+            ids: Union[int, List[int]],
+            skip_special_tokens: bool = False) -> Union[str, List[str]]:
+        """
+        Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
+        added tokens.
+        Args:
+            ids (`int` or `List[int]`):
+                The token id (or token ids) to convert to tokens.
+            skip_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not to remove special tokens in the decoding.
+        Returns:
+            `str` or `List[str]`: The decoded token(s).
+        """
+        if isinstance(ids, int):
+            if ids in self.added_tokens_decoder:
+                return self.added_tokens_decoder[ids]
+            else:
+                return self._convert_id_to_token(ids)
+        tokens = []
+        for index in ids:
+            index = int(index)
+            if skip_special_tokens and index in self.all_special_ids and index != 2:
+                continue
+            if index in self.added_tokens_decoder:
+                tokens.append(self.added_tokens_decoder[index])
+            else:
+                tokens.append(self._convert_id_to_token(index))
+        return tokens
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        # for token in
+        # tokens = tokens or self.ids_to_tokens(ids)
+        # tokens = [token for token in tokens if not self._is_special(token)]
+
+        text = ''
+        for i, token in enumerate(tokens):
+            if token[:2] == '##':
+                text += token[2:]
+            elif len(token) == 1 and _is_chinese_char(ord(token)):
+                text += token
+            elif len(token) == 1 and _is_punctuation(token):
+                text += token
+                text += ' '
+            elif i > 0 and _is_chinese_char(ord(text[-1])):
+                text += token
+            elif tokens == "</s>":
+                continue
+            else:
+                text += ' '
+                text += token
+
+        text = re.sub(' +', ' ', text)
+        text = re.sub('\' (re|m|s|t|ve|d|ll) ', '\'\\1 ', text)
+        punctuation = re.sub(' +', '', self._cjk_punctuation()).strip() + '+-/={(<['
+        punctuation_regex = '|'.join([re.escape(p) for p in punctuation])
+        punctuation_regex = '(%s) ' % punctuation_regex
+        text = re.sub(punctuation_regex, '\\1', text)
+        text = re.sub(r'(\d\.) (\d)', '\\1\\2', text)
+
+        return text.strip()
+        # out_string = " ".join(tokens).replace(" ##", "").strip()
+
+    def build_inputs_with_special_tokens(
+            self,
+            token_ids_0: List[int],
+            token_ids_1: Optional[List[int]] = None) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
+        and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence:
+        - single sequence: `X </s>`
+        - pair of sequences: `A B </s>` (not intended use)
+        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+        separator.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return token_ids_0 + [self.eos_token_id]
+        return token_ids_0 + token_ids_1 + [self.eos_token_id]
+
+    def _special_token_mask(self, seq):
+        all_special_ids = set(
+            self.all_special_ids)  # call it once instead of inside list comp
+        # all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
+
+        return [1 if x in all_special_ids else 0 for x in seq]
+
+    def get_special_tokens_mask(
+            self,
+            token_ids_0: List[int],
+            token_ids_1: Optional[List[int]] = None,
+            already_has_special_tokens: bool = False) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return self._special_token_mask(token_ids_0)
+        elif token_ids_1 is None:
+            return self._special_token_mask(token_ids_0) + [self.eos_token_id]
+        else:
+            return self._special_token_mask(token_ids_0 +
+                                            token_ids_1) + [self.eos_token_id]
+
+    def num_special_tokens_to_add(self, pair=False):
+        """Just EOS"""
+        return 1
+
+    def save_vocabulary(self,
+                        save_directory: str,
+                        filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory,
+                (filename_prefix + "-" if filename_prefix else "") +
+                VOCAB_FILES_NAMES["vocab_file"])
+        else:
+            vocab_file = (filename_prefix +
+                          "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(),
+                                             key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!")
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file, )
+
+
+class BasicTokenizer(object):
+    """
+    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+    Args:
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents: (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+    """
+
+    def __init__(self,
+                 do_lower_case=True,
+                 never_split=None,
+                 tokenize_chinese_chars=True,
+                 strip_accents=None):
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+        self.strip_accents = strip_accents
+
+    def tokenize(self, text, never_split=None):
+        """
+        Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
+        WordPieceTokenizer.
+        Args:
+            never_split (`List[str]`, *optional*)
+                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(
+            set(never_split)) if never_split else self.never_split
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if token not in never_split:
+                if self.do_lower_case:
+                    token = token.lower()
+                    if self.strip_accents is not False:
+                        token = self._run_strip_accents(token)
+                elif self.strip_accents:
+                    token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if never_split is not None and text in never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """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.
+        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 _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through *BasicTokenizer*.
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens

requirements.txt

@@ -0,0 +1,7 @@
+torch
+tensorflow
+
+transformers
+peft
+pandas
+sentence-transformers

util.py

@@ -0,0 +1,82 @@
+import torch
+
+
+class DeviceMap:
+    __top_layer: str
+    __device_map: dict
+    __total_layers: int
+    __layers: int
+
+    def __init__(self, model=None):
+        if model == "LLaMA":
+            self.__top_layer = "model"
+            self.__device_map = {
+                "model.embed_tokens": 0,
+                "model.norm": 0,
+                "lm_head": 0,
+            }
+            self.__total_layers = 34
+            self.__layers = 32
+
+        elif model == "ChatGLM":
+            self.__top_layer = "transformer"
+            self.__device_map = {
+                "transformer.word_embeddings": 0,
+                "transformer.final_layernorm": 0,
+                "lm_head": 0,
+            }
+            self.__total_layers = 30
+            self.__layers = 28
+
+        else:
+            self.__top_layer = ""
+            self.__device_map = {"": 0}
+            self.__total_layers = 0
+            self.__layers = 0
+
+    def get(self):
+        top_layer = self.__top_layer
+        total_layers = self.__total_layers
+        layers = self.__layers
+        device_map = self.__device_map
+
+        world_size = torch.cuda.device_count()
+
+        free_gpu_mem = []
+        for i in range(world_size):
+            torch.cuda.set_device(i)
+            free_gpu_mem.append(torch.cuda.mem_get_info()[0])
+
+        min_id = min(enumerate(free_gpu_mem), key=lambda x: x[1])[0]
+        max_id = max(enumerate(free_gpu_mem), key=lambda x: x[1])[0]
+
+        totol_mem = sum(free_gpu_mem)
+
+        world_layers = {
+            id: int(round(total_layers * (mem / totol_mem)))
+            for id, mem in enumerate(free_gpu_mem)
+        }
+
+        diff = total_layers - sum(world_layers.values())
+        world_layers[max_id if diff > 0 else min_id] += diff
+
+        cnt = total_layers - layers
+        gpu_id = 0
+
+        for i in range(layers):
+            if cnt < world_layers[gpu_id]:
+                cnt += 1
+            else:
+                gpu_id += 1
+                cnt = 1
+            device_map[f"{top_layer}.layers.{i}"] = gpu_id
+
+        return device_map
+
+    def peft(self):
+        prefix = "base_model.model"
+        device_map = self.get()
+        perf_device_map = {"": 0}
+        for k, v in device_map.items():
+            perf_device_map[f"{prefix}.{k}"] = v
+        return perf_device_map