|
import regex as re |
|
import base64 |
|
import os |
|
import tiktoken |
|
from typing import List, Optional, Union, Dict |
|
from transformers import PreTrainedTokenizer |
|
from transformers.utils import PaddingStrategy |
|
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding |
|
|
|
|
|
class ChatGLM4Tokenizer(PreTrainedTokenizer): |
|
vocab_files_names = {"vocab_file": "tokenizer.model"} |
|
model_input_names = ["input_ids", "attention_mask", "position_ids"] |
|
|
|
def __init__( |
|
self, |
|
vocab_file, |
|
clean_up_tokenization_spaces=False, |
|
**kwargs |
|
): |
|
self.name = "GLM4Tokenizer" |
|
self.vocab_file = vocab_file |
|
pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" |
|
self.pat_str = re.compile(pat_str) |
|
|
|
mergeable_ranks = {} |
|
with open(vocab_file) as f: |
|
for line in f: |
|
token, rank = line.strip().split() |
|
rank = int(rank) |
|
token = base64.b64decode(token) |
|
mergeable_ranks[token] = rank |
|
|
|
self.mergeable_ranks = mergeable_ranks |
|
|
|
self.tokenizer = tiktoken.Encoding( |
|
name="my_tokenizer", |
|
pat_str=pat_str, |
|
mergeable_ranks=mergeable_ranks, |
|
special_tokens={} |
|
) |
|
self.decoder = {rank: token for token, rank in mergeable_ranks.items()} |
|
self.n_words = len(self.decoder) |
|
|
|
super().__init__( |
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
|
**kwargs |
|
) |
|
|
|
@property |
|
def vocab_size(self): |
|
return self.n_words |
|
|
|
def get_vocab(self): |
|
""" Returns vocab as a dict """ |
|
vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} |
|
vocab.update(self.added_tokens_encoder) |
|
return vocab |
|
|
|
def convert_tokens_to_string(self, tokens: List[Union[bytes, str, int]]) -> str: |
|
""" |
|
Converts a sequence of tokens in a single string. |
|
""" |
|
text = "" |
|
temp = b"" |
|
for t in tokens: |
|
if isinstance(t, int): |
|
t = chr(t) |
|
if isinstance(t, str): |
|
if temp: |
|
text += temp.decode("utf-8", errors="replace") |
|
elif isinstance(t, bytes): |
|
temp += t |
|
else: |
|
raise TypeError("token should only be of type int, bytes or str") |
|
if temp: |
|
text += temp.decode("utf-8", errors="replace") |
|
return text |
|
|
|
def _tokenize(self, text, **kwargs): |
|
tokens = [] |
|
ids = self.tokenizer.encode(text) |
|
for t in ids: |
|
tokens.append(self.decoder[t]) |
|
return tokens |
|
|
|
def _convert_token_to_id(self, token): |
|
""" Converts a token (str) in an id using the vocab. """ |
|
return self.mergeable_ranks[token] |
|
|
|
def _convert_id_to_token(self, index): |
|
"""Converts an index (integer) in a token (str) using the vocab.""" |
|
return self.decoder.get(index, "") |
|
|
|
def save_vocabulary(self, save_directory, filename_prefix=None): |
|
""" |
|
Save the vocabulary and special tokens file to a directory. |
|
|
|
Args: |
|
save_directory (`str`): |
|
The directory in which to save the vocabulary. |
|
filename_prefix (`str`, *optional*): |
|
An optional prefix to add to the named of the saved files. |
|
|
|
Returns: |
|
`Tuple(str)`: Paths to the files saved. |
|
""" |
|
if os.path.isdir(save_directory): |
|
vocab_file = os.path.join( |
|
save_directory, self.vocab_files_names["vocab_file"] |
|
) |
|
else: |
|
vocab_file = save_directory |
|
|
|
with open(self.vocab_file, 'rb') as fin: |
|
proto_str = fin.read() |
|
|
|
with open(vocab_file, "wb") as writer: |
|
writer.write(proto_str) |
|
|
|
return (vocab_file,) |
|
|
|
def get_prefix_tokens(self): |
|
prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")] |
|
return prefix_tokens |
|
|
|
def build_single_message(self, role, metadata, message, tokenize=True): |
|
assert role in ["system", "user", "assistant", "observation"], role |
|
if tokenize: |
|
role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n", |
|
disallowed_special=()) |
|
message_tokens = self.tokenizer.encode(message, disallowed_special=()) |
|
tokens = role_tokens + message_tokens |
|
return tokens |
|
else: |
|
return str(f"<|{role}|>{metadata}\n{message}") |
|
|
|
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 sequence for sequence classification tasks by concatenating and |
|
adding special tokens. A BERT sequence has the following format: |
|
|
|
- single sequence: `[CLS] X [SEP]` |
|
- pair of sequences: `[CLS] A [SEP] B [SEP]` |
|
|
|
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. |
|
""" |
|
prefix_tokens = self.get_prefix_tokens() |
|
token_ids_0 = prefix_tokens + token_ids_0 |
|
if token_ids_1 is not None: |
|
token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")] |
|
return token_ids_0 |
|
|
|
def _pad( |
|
self, |
|
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], |
|
max_length: Optional[int] = None, |
|
padding_side: str = "left", |
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, |
|
pad_to_multiple_of: Optional[int] = None, |
|
return_attention_mask: Optional[bool] = None, |
|
) -> dict: |
|
""" |
|
Pad encoded inputs (on left/right and up to predefined length or max length in the batch) |
|
|
|
Args: |
|
encoded_inputs: |
|
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). |
|
max_length: maximum length of the returned list and optionally padding length (see below). |
|
Will truncate by taking into account the special tokens. |
|
padding_strategy: PaddingStrategy to use for padding. |
|
|
|
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch |
|
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default) |
|
- PaddingStrategy.DO_NOT_PAD: Do not pad |
|
The tokenizer padding sides are defined in self.padding_side: |
|
|
|
- 'left': pads on the left of the sequences |
|
- 'right': pads on the right of the sequences |
|
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. |
|
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability |
|
`>= 7.5` (Volta). |
|
return_attention_mask: |
|
(optional) Set to False to avoid returning attention mask (default: set to model specifics) |
|
""" |
|
|
|
|
|
required_input = encoded_inputs[self.model_input_names[0]] |
|
seq_length = len(required_input) |
|
|
|
if padding_strategy == PaddingStrategy.LONGEST: |
|
max_length = len(required_input) |
|
|
|
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): |
|
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of |
|
|
|
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length |
|
|
|
|
|
if "attention_mask" not in encoded_inputs: |
|
encoded_inputs["attention_mask"] = [1] * seq_length |
|
|
|
if "position_ids" not in encoded_inputs: |
|
encoded_inputs["position_ids"] = list(range(seq_length)) |
|
|
|
if needs_to_be_padded: |
|
difference = max_length - len(required_input) |
|
|
|
if "attention_mask" in encoded_inputs: |
|
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] |
|
if "position_ids" in encoded_inputs: |
|
encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] |
|
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input |
|
|
|
return encoded_inputs |
