189 lines
6.1 KiB
Python
189 lines
6.1 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) 2022 Zhipu.AI
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from typing import List, Union
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import torch
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from transformers import AutoTokenizer
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from transformers.models.gpt2 import GPT2TokenizerFast
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def encode_whitespaces(text, start_extra_id: int, max_len: int):
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""" Encode whitespaces to extra tokens in GPT-J.
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>>> encode_whitespaces('a\\n b\\n c', 10, 10)
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'a\\n<|extratoken_10|>b\\n<|extratoken_11|>c'
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"""
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def push_acc_space(acc_len: int, text: str):
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if acc_len == 0:
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return text
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if acc_len == 1:
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return text + ' '
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assert acc_len <= max_len, f'Max whitespace run length {max_len}, but found {acc_len}'
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extra_id = start_extra_id - 2 + acc_len
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extra_token = f'<|extratoken_{extra_id}|>'
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return text + extra_token
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acc_len = 0
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res = ''
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for ch in text:
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if ch == ' ':
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acc_len += 1
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if acc_len == max_len:
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res = push_acc_space(acc_len, res)
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acc_len = 0
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else:
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res = push_acc_space(acc_len, res)
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acc_len = 0
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res = res + ch
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res = push_acc_space(acc_len, res)
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return res
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def decode_whitespaces(text: str, start_extra_id: int, max_len: int):
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""" Decode the whitespace-encoded strings produced by encode_whitespace.
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>>> text = 'a\\n b\\n c'
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>>> s, l = 10, 10
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>>> text == decode_whitespaces(encode_whitespaces(text, s, l), s, l)
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True
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"""
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for l in range(2, max_len + 1): # noqa
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token_id = start_extra_id - 2 + l
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token = f'<|extratoken_{token_id}|>'
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text = text.replace(token, ' ' * l)
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return text
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class Code13BDictionary(object):
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def __init__(
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self,
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dict_file: str,
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extra_token_ids: List[str] = None,
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pad_to_vocab_size: int = -1,
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):
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self._idx = dict()
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self._count = dict()
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self._num_symbols = 0
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self._symbols = []
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self._add_symbol('<s>', 0)
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self._add_symbol('<pad>', 0)
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self._add_symbol('</s>', 0)
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self._add_symbol('<unk>', 0)
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self._load_dict(dict_file)
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if extra_token_ids is None:
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extra_token_ids = [str(x) for x in range(50257, 50400)
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] # follows GPT-J settings
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for token_id in extra_token_ids:
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self._add_symbol(token_id, 0)
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if pad_to_vocab_size > 0:
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self._pad_to_vocab_size(pad_to_vocab_size)
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def _pad_to_vocab_size(self, vocab_size: int):
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num_pad = vocab_size - len(self)
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if num_pad <= 0:
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return
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for i in range(1, num_pad + 1):
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self._add_symbol('vocab_pad_token{}'.format(i), 0)
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def _load_dict(self, dict_file: str):
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with open(dict_file, 'r') as f:
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for line in f:
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line = line.strip()
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if line == '' or line.startswith('#'):
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continue
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sym, count = line.split()
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self._add_symbol(sym, int(count))
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def _add_symbol(self, sym: str, count: int):
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self._idx[sym] = self._num_symbols
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self._count[sym] = count
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self._symbols.append(sym)
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self._num_symbols += 1
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def __len__(self):
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return self._num_symbols
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def index(self, sym: str):
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return self._idx[sym]
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def string(self, idx: int):
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return self._symbols[idx]
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def map_token(self, token: Union[int, str]):
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if isinstance(token, int):
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token = str(token)
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return self.index(token)
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def map_tokens(self, tokens):
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return [self.map_token(token) for token in tokens]
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def decode_tokens(self, tokens):
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decoded = [
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'50256' if token == 50256 else self.string(token)
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for token in tokens
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]
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return [int(x) for x in decoded if not x.startswith('vocab_pad_token')]
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class CodeGeeXTokenizer(object):
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def __init__(
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self,
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tokenizer: GPT2TokenizerFast = None,
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tokenizer_path: str = 'EleutherAI/gpt-j-6B',
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start_extra_id: int = 10,
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max_len: int = 10,
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mode='codegeex-13b',
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dict_file: str = None,
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):
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self.tokenizer = tokenizer if tokenizer is not None else AutoTokenizer.from_pretrained(
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tokenizer_path)
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if mode not in ['codegeex-13b', 'codegeex-python-13b']:
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raise ValueError(
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f"Invalid mode {mode}, choose from ['codegeex-13b', 'codegeex-python-13b']"
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)
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self.start_extra_id = start_extra_id
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self.max_len = max_len
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self.mode = mode
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if dict_file is not None:
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self.code_dict = Code13BDictionary(
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dict_file, pad_to_vocab_size=51200
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) if self.mode == 'codegeex-python-13b' else None
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else:
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self.code_dict = None
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self.eos_token_id = self.tokenizer.eos_token_id
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def encode_code(self, code: str):
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if self.mode == 'codegeex-13b':
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code = encode_whitespaces(code, self.start_extra_id, self.max_len)
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input_ids = self.tokenizer(
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code, is_split_into_words=False).input_ids
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elif self.mode == 'codegeex-python-13b':
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code = encode_whitespaces(code, self.start_extra_id, self.max_len)
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input_ids = self.code_dict.map_tokens(self.tokenizer.encode(code))
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input_ids = torch.LongTensor(input_ids).reshape(1, -1)
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return input_ids
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def decode_code(self, input_ids):
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if self.mode == 'codegeex-13b':
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text = self.tokenizer.decode(input_ids, skip_special_tokens=False)
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output_code = decode_whitespaces(text, self.start_extra_id,
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self.max_len)
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elif self.mode == 'codegeex-python-13b':
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input_ids = [self.code_dict.decode_tokens(input_ids.tolist()[0])]
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text = self.tokenizer.decode(input_ids, skip_special_tokens=False)
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output_code = decode_whitespaces(text, self.start_extra_id,
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self.max_len)
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return output_code
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