Spaces:
Sleeping
Sleeping
File size: 8,846 Bytes
341de97 7e8d9b9 341de97 7e8d9b9 341de97 7e8d9b9 341de97 7e8d9b9 341de97 1f125f1 341de97 7e8d9b9 341de97 7e8d9b9 341de97 cc8b2eb 1f125f1 7e8d9b9 341de97 7e8d9b9 341de97 cc8b2eb 341de97 0186ed1 7e8d9b9 341de97 11c7796 341de97 cc8b2eb ee83d59 1f125f1 341de97 7e8d9b9 341de97 cc8b2eb 341de97 1f125f1 cc8b2eb 341de97 ee83d59 162b7a6 ee83d59 11c7796 0186ed1 ee83d59 11c7796 ee83d59 11c7796 ee83d59 11c7796 0186ed1 11c7796 ee83d59 0186ed1 11c7796 341de97 7e8d9b9 341de97 ee83d59 341de97 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
import os
from typing import Union
import torch
from transformers import LogitsProcessor
from seed_scheme_factory import SeedSchemeFactory
from utils import bytes_to_base, base_to_bytes, get_values_per_byte
class BaseProcessor(object):
def __init__(
self,
msg_base: int,
vocab: list[int],
device: torch.device,
seed_scheme: str,
window_length: int = 1,
salt_key: Union[int, None] = None,
private_key: Union[int, None] = None,
):
"""
Args:
msg_base: base of the message.
vocab: vocabulary list.
device: device to load processor.
seed_scheme: scheme used to compute the seed.
window_length: length of window to compute the seed.
salt_key: salt to add to the seed.
private_key: private key used to compute the seed.
"""
# Universal parameters
self.msg_base = msg_base
self.vocab = vocab
self.vocab_size = len(vocab)
self.device = device
# Seed parameters
seed_fn = SeedSchemeFactory.get_instance(
seed_scheme,
salt_key=salt_key,
private_key=private_key,
)
if seed_fn is None:
raise ValueError(f'Seed scheme "{seed_scheme}" is invalid')
else:
self.seed_fn = seed_fn
self.window_length = window_length
# Initialize RNG, always use cpu generator
self.rng = torch.Generator(device="cpu")
# Compute the ranges of each value in base
self.ranges = torch.zeros((self.msg_base + 1), dtype=torch.int64).to(
self.device
)
chunk_size = self.vocab_size / self.msg_base
r = self.vocab_size % self.msg_base
self.ranges[1:] = chunk_size
self.ranges[1 : r + 1] += 1
self.ranges = torch.cumsum(self.ranges, dim=0)
def _seed_rng(self, input_ids: torch.Tensor):
"""
Set the seed for the rng based on the current sequences.
Args:
input_ids: id in the input sequence.
"""
seed = self.seed_fn(input_ids[-self.window_length :])
self.rng.manual_seed(seed)
def _get_valid_list_ids(self, input_ids: torch.Tensor, value: int):
"""
Get ids of tokens in the valid list for the current sequences.
"""
self._seed_rng(input_ids)
vocab_perm = torch.randperm(
self.vocab_size, generator=self.rng, device="cpu"
).to(self.device)
vocab_list = vocab_perm[self.ranges[value] : self.ranges[value + 1]]
return vocab_list
def _get_value(self, input_ids: torch.Tensor):
"""
Check whether the token is in the valid list.
"""
self._seed_rng(input_ids[:-1])
vocab_perm = torch.randperm(
self.vocab_size, generator=self.rng, device="cpu"
).to(self.device)
cur_token = input_ids[-1]
cur_id = (vocab_perm == cur_token).nonzero(as_tuple=True)[0]
value = (cur_id < self.ranges).type(torch.int).argmax().item() - 1
return value
class EncryptorLogitsProcessor(LogitsProcessor, BaseProcessor):
def __init__(
self,
prompt_ids: torch.Tensor,
msg: bytes,
delta: float,
tokenizer,
start_pos: int = 0,
*args,
**kwargs,
):
"""
Args:
msg: message to hide in the text.
delta: bias add to scores of token in valid list.
"""
super().__init__(*args, **kwargs)
# if prompt_ids.size(0) != 1:
# raise RuntimeError(
# "EncryptorLogitsProcessor does not support multiple prompts input."
# )
self.prompt_size = prompt_ids.size(1)
self.start_pos = start_pos
self.raw_msg = msg
self.msg = bytes_to_base(msg, self.msg_base)
self.delta = delta
self.tokenizer = tokenizer
special_tokens = [
tokenizer.bos_token_id,
tokenizer.eos_token_id,
tokenizer.sep_token_id,
tokenizer.pad_token_id,
tokenizer.cls_token_id,
]
special_tokens = [x for x in special_tokens if x is not None]
self.special_tokens = torch.tensor(special_tokens, device=self.device)
def __call__(
self, input_ids_batch: torch.LongTensor, scores_batch: torch.FloatTensor
):
# If the whole message is hidden already, then just return the raw scores.
for i, input_ids in enumerate(input_ids_batch):
cur_pos = input_ids.size(0)
msg_ptr = cur_pos - (self.prompt_size + self.start_pos)
if msg_ptr < 0 or msg_ptr >= len(self.msg):
continue
scores_batch[i] = self._add_bias_to_valid_list(
input_ids, scores_batch[i], self.msg[msg_ptr]
)
return scores_batch
def _add_bias_to_valid_list(
self, input_ids: torch.Tensor, scores: torch.Tensor, value: int
):
"""
Add the bias (delta) to the valid list tokens
"""
ids = torch.cat(
[self._get_valid_list_ids(input_ids, value), self.special_tokens]
)
scores[ids] = scores[ids] + self.delta
return scores
def get_message_len(self):
return len(self.msg)
def __map_input_ids(self, input_ids: torch.Tensor, base_arr, byte_arr):
byte_enc_msg = [-1 for _ in range(input_ids.size(0))]
base_enc_msg = [-1 for _ in range(input_ids.size(0))]
base_msg = [-1 for _ in range(input_ids.size(0))]
byte_msg = [-1 for _ in range(input_ids.size(0))]
values_per_byte = get_values_per_byte(self.msg_base)
start = self.start_pos % values_per_byte
for i, b in enumerate(base_arr):
base_enc_msg[i] = base_arr[i]
byte_enc_msg[i] = byte_arr[(i - start) // values_per_byte]
for i, b in enumerate(self.msg):
if i + self.start_pos >= len(base_msg):
break
base_msg[i + self.start_pos] = b
byte_msg[i + self.start_pos] = self.raw_msg[i // values_per_byte]
return base_msg, byte_msg, base_enc_msg, byte_enc_msg
def validate(self, input_ids_batch: torch.Tensor):
msgs_rates = []
tokens_infos = []
for input_ids in input_ids_batch:
# Initialization
base_arr = []
# Loop and obtain values of all tokens
for i in range(0, input_ids.size(0)):
base_arr.append(self._get_value(input_ids[: i + 1]))
values_per_byte = get_values_per_byte(self.msg_base)
# Transform the values to bytes
start = self.start_pos % values_per_byte
byte_arr = base_to_bytes(base_arr[start:], self.msg_base)
# Construct the
cnt = 0
enc_msg = byte_arr[self.start_pos // values_per_byte :]
for i in range(min(len(enc_msg), len(self.raw_msg))):
if self.raw_msg[i] == enc_msg[i]:
cnt += 1
msgs_rates.append(cnt / len(self.raw_msg))
base_msg, byte_msg, base_enc_msg, byte_enc_msg = (
self.__map_input_ids(input_ids, base_arr, byte_arr)
)
tokens = []
input_strs = [self.tokenizer.decode([input]) for input in input_ids]
for i in range(len(base_enc_msg)):
tokens.append(
{
"token": input_strs[i],
"base_enc": base_enc_msg[i],
"byte_enc": byte_enc_msg[i],
"base_msg": base_msg[i],
"byte_msg": byte_msg[i],
"byte_id": (i - start) // values_per_byte,
}
)
tokens_infos.append(tokens)
return msgs_rates, tokens_infos
class DecryptorProcessor(BaseProcessor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def decrypt(self, input_ids_batch: torch.Tensor):
"""
Decrypt the text sequences.
"""
shift_msg = []
for shift in range(get_values_per_byte(self.msg_base)):
msg = []
bytes_msg = []
for i, input_ids in enumerate(input_ids_batch):
msg.append(list())
for j in range(shift, len(input_ids)):
# TODO: this could be slow. Considering reimplement this.
value = self._get_value(input_ids[: j + 1])
msg[i].append(value)
bytes_msg.append(base_to_bytes(msg[i], self.msg_base))
shift_msg.append(bytes_msg)
return shift_msg
|