Spaces:
Running on Zero
Running on Zero
File size: 8,411 Bytes
7b592f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | # Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
import json
from pathlib import Path
from typing import Optional, Union, Iterable, Iterator
from src.audiointeraction.utils import fix_and_load_json
import torch
class Tokenizer:
def __init__(self, checkpoint_dir: Union[Path, str]) -> None:
checkpoint_dir = Path(checkpoint_dir)
if not checkpoint_dir.exists():
raise NotADirectoryError(f"The checkpoint directory does not exist: {str(checkpoint_dir)}")
self.model_name = checkpoint_dir.stem
self.use_bos = self.check_if_bos_token_used(checkpoint_dir)
self.bos_id = None
self.eos_id = None
# some checkpoints have both files, `.json` takes precedence
if (vocabulary_path := checkpoint_dir / "tokenizer.json").is_file():
from tokenizers import Tokenizer as HFTokenizer
self.processor = HFTokenizer.from_file(str(vocabulary_path))
self.backend = "huggingface"
if (special_tokens_path := checkpoint_dir / "tokenizer_config.json").is_file():
with open(special_tokens_path, encoding="utf-8") as fp:
config = json.load(fp)
bos_token = config.get("bos_token")
eos_token = config.get("eos_token")
if bos_token is not None and isinstance(bos_token, dict):
bos_token = bos_token.get("content")
if eos_token is not None and isinstance(eos_token, dict):
eos_token = eos_token.get("content")
self.bos_id = self.token_to_id(bos_token) if bos_token is not None else None
self.eos_id = self.token_to_id(eos_token) if eos_token is not None else None
if (special_tokens_path := checkpoint_dir / "generation_config.json").is_file():
try:
with open(special_tokens_path, encoding="utf-8") as fp:
config = json.load(fp)
except json.JSONDecodeError: # Some files like the Llama 3.2 one have bugs
with open(special_tokens_path, encoding="utf-8") as fp:
json_string = fp.read()
config = fix_and_load_json(json_string)
if self.bos_id is None:
self.bos_id = config.get("bos_token_id")
if self.eos_id is None:
self.eos_id = config.get("eos_token_id")
elif (vocabulary_path := checkpoint_dir / "tokenizer.model").is_file():
from sentencepiece import SentencePieceProcessor
self.processor = SentencePieceProcessor(model_file=str(vocabulary_path))
self.backend = "sentencepiece"
self.bos_id = self.processor.bos_id()
self.eos_id = self.processor.eos_id()
else:
raise NotImplementedError
# NOTE: A temporary fix until it's resolved on Tokenizers side.
# LlaMA tokenizer strips leading spaces if to decode a single token at a time.
# https://github.com/huggingface/transformers/issues/31643
self.apply_decoding_fix = None
if (config_path := checkpoint_dir / "tokenizer_config.json").is_file():
with open(config_path, encoding="utf-8") as fp:
self.apply_decoding_fix = "LlamaTokenizer" in json.load(fp)["tokenizer_class"]
@property
def vocab_size(self) -> int:
if self.backend == "huggingface":
return self.processor.get_vocab_size(with_added_tokens=False)
if self.backend == "sentencepiece":
return self.processor.vocab_size()
raise RuntimeError
def token_to_id(self, token: str) -> int:
if self.backend == "huggingface":
id_ = self.processor.token_to_id(token)
elif self.backend == "sentencepiece":
id_ = self.processor.piece_to_id(token)
else:
raise RuntimeError
if id_ is None:
raise ValueError(f"token {token!r} not found in the collection.")
return id_
def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool:
if not (tokenizer_config_path := checkpoint_dir / "tokenizer_config.json").is_file():
return False
with open(tokenizer_config_path, encoding="utf-8") as fp:
config = json.load(fp)
# for LlaMA-3 tokenizer there is no `add_bos_token` at all and `tokenizer_class` is only
# `PreTrainedTokenizerFast`
if checkpoint_dir.stem.startswith(("Meta-Llama-3", "Llama-3")):
return True
if checkpoint_dir.stem.startswith("SmolLM2") and checkpoint_dir.name.endswith("Instruct"):
return True
if "add_bos_token" in config:
return config["add_bos_token"]
# if `add_bos_token` isn't in the config file, but LLaMA tokenizer is used - return True.
# ex: https://huggingface.co/stabilityai/StableBeluga2/blob/main/tokenizer_config.json#L2
return config.get("tokenizer_class") == "LlamaTokenizer"
def encode(
self,
string: str,
device: Optional[torch.device] = None,
bos: Optional[bool] = None,
eos: bool = False,
max_length: int = -1,
) -> torch.Tensor:
if self.backend == "huggingface":
tokens = self.processor.encode(string).ids
elif self.backend == "sentencepiece":
tokens = self.processor.encode(string)
else:
raise RuntimeError(f"`{self.backend}` is not supported.")
if tokens is None:
raise ValueError("`self.processor` returned tokens of None value.")
if bos or (bos is None and self.use_bos):
if self.bos_id is None:
raise NotImplementedError("This tokenizer does not have a defined bos token.")
if not tokens or tokens[0] != self.bos_id:
tokens = [self.bos_id] + tokens
# if the processor misbehaves and adds `bos` token no matter what
elif tokens and tokens[0] == self.bos_id:
tokens = tokens[1:]
if eos and (not tokens or tokens[-1] != self.eos_id):
tokens = tokens + [self.eos_id]
# if the processor misbehaves and adds `eos` token no matter what
elif tokens and tokens[-1] == self.eos_id:
tokens = tokens[:-1]
if max_length > 0:
tokens = tokens[:max_length]
return torch.tensor(tokens, dtype=torch.int, device=device)
def decode(self, tensor: torch.Tensor) -> str:
tokens = [tensor.item()] if tensor.ndim == 0 else tensor.tolist()
if len(tokens) == 1 and self.apply_decoding_fix:
dummy_token_id = 33 # \x1e
dummy_token = self.processor.decode([dummy_token_id])
if dummy_token != "\x1e":
dummy_token_id = 165 # \x1e is different in salamandra tokenizers
dummy_token = self.processor.decode([dummy_token_id])
return self.processor.decode([dummy_token_id] + tokens)[len(dummy_token) :]
return self.processor.decode(tokens)
def decode_stream(self, token_stream: Iterable[torch.Tensor], device: Optional[torch.device] = None) -> Iterator[str]:
if self.backend == "huggingface":
try:
for token in token_stream:
yield self.decode(token)
except KeyboardInterrupt:
return
elif self.backend == "sentencepiece":
# TODO: Is there a way to not have to do this?
# This may actually affect our tokens per second.
# sentencepiece does not support decoding token-by-token because it adds spaces based on the surrounding tokens
# meaning that we need to decode everything each time
so_far = torch.tensor([], dtype=torch.long, device=device)
decoded_so_far = ""
try:
for token in token_stream:
so_far = so_far.to(device=token.device)
so_far = torch.cat((so_far, token.view(-1)))
decoded_new = self.decode(so_far)
yield decoded_new[len(decoded_so_far) :]
decoded_so_far = decoded_new
except KeyboardInterrupt:
return
else:
raise NotImplementedError(self.backend)
|