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28eb83d | 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 | from abc import ABC, abstractmethod
import copy
import logging
from typing import Any, Callable, Dict, List, Tuple, Union
from xllmx.model.tokenizer import Tokenizer
logger = logging.getLogger(__name__)
class LabelAllZeroError(Exception):
def __init__(self, message=None):
self.message = message
def __str__(self):
return f"LabelAllZeroError: {self.message}"
class ItemProcessorBase(ABC):
@abstractmethod
def process_item(self, data_item: dict, training_mode=False) -> Tuple[List, List]:
raise NotImplementedError
def predict_item_token_length(self, data_item: dict) -> int:
"""
estimate the token length of the data item for gathering items of similar lengths into a batch
"""
return 1
class MMConvItemProcessor(ItemProcessorBase):
def __init__(
self,
transform: Dict[str, Callable[[Any], Dict]],
media_symbols: List[str],
tokenizer,
conv_template,
):
self.transform = transform
logger.info(f"transform:\n{self.transform}")
self.media_symbols = media_symbols
logger.info(f"media_symbols:\n{self.media_symbols}")
if isinstance(tokenizer, str):
self.tokenizer = Tokenizer(model_path=tokenizer)
else:
self.tokenizer = copy.deepcopy(tokenizer)
# todo should not already exist
self.tokenizer.tokenizer.add_tokens(media_symbols)
self.d_media_symbol2token = {}
self.d_media_token2symbol = {}
for media_symbol in media_symbols:
tokenized_symbol = self.tokenizer.encode(media_symbol, bos=False, eos=False)
assert len(tokenized_symbol) == 1
self.d_media_symbol2token[media_symbol] = tokenized_symbol[0]
self.d_media_token2symbol[tokenized_symbol[0]] = media_symbol
# implicit_at_beginning means media without explict location specification are arranged right after bos token
# if false, then these medias are arranged at the beginning of the first question
self.implicit_at_beginning = False
self.conv_template = conv_template
def collect_and_process_media(self, data_item):
"""
this function receives a raw piece of data (e.g. read from `.json` data file),
and returns d_media, containing the prepared media readily usable by model
YOU MAY OVERRIDE THIS FUNCTION TO SUPPORT COMPLEX LOADING OF VARIOUS FORMS OF DATA
"""
d_media = {}
for media_symbol in self.media_symbols:
if media_symbol in data_item:
l_media = data_item[media_symbol] # a list of media paths
elif media_symbol.lstrip("<|").rstrip("|>") in data_item:
l_media = data_item[media_symbol.lstrip("<|").rstrip("|>")]
else:
l_media = []
if not isinstance(l_media, list): # data with only one media, in format {"image": image_name, ...}
l_media = [l_media]
d_media[media_symbol] = []
for media in l_media:
media = self.transform[media_symbol](media)
assert isinstance(media, Dict)
media["type"] = media_symbol
d_media[media_symbol].append(media)
return d_media
def replace_media_token_with_media(
self, tokens: List[int], labels: Union[List[int], None], d_media: Dict[str, List]
):
d_media_counter = {key: 0 for key in d_media}
for i, t in enumerate(tokens):
if t in self.d_media_token2symbol:
media_symbol = self.d_media_token2symbol[t]
media = d_media[media_symbol][d_media_counter[media_symbol]]
d_media_counter[media_symbol] += 1
tokens[i] = media
media["to_predict"] = labels[i] > 0
assert all([d_media_counter[key] == len(d_media[key]) for key in d_media])
if labels is not None:
return tokens, labels
else:
return tokens
@staticmethod
def insert_implicit_media_symbol_in_q1(conv_list: List[Dict], d_media: Dict):
"""
Add the media tokens to the beginning of the first instruction from
human. This logic may be more reasonable. However, it is incompatible
with old-version Accessory models, which are trained with image tokens
inserted directly behind the first token (<bos>).
:param conv_list: [{"from": "human", "value": "..."}, {"from": "gpt", "value": "..."}, ...]
:param d_media: a dict of media for all media types
"""
conv_list = copy.deepcopy(conv_list)
for media_symbol, l_media in d_media.items():
media_symbol_count = "".join([_["value"] for _ in conv_list if _["value"] is not None]).count(media_symbol)
if media_symbol_count > 0:
assert media_symbol_count == len(
l_media
), f"{media_symbol_count} {media_symbol} exists in text, but {len(l_media)} actual media are given"
else:
conv_list[0]["value"] = (media_symbol + " ") * len(l_media) + conv_list[0]["value"]
return conv_list
@staticmethod
def insert_implicit_media_symbol_at_beginning(conv: str, d_media: Dict):
"""
Legacy versions of LLaMA2-Accessory handled media in a non-interleaved
manner, where image tokens are inserted directly behind the first token,
namely <bos>. To support interleaved media comprehension and generation,
Accessory now supports the explicit specification of media occurrence,
which is achieved by adding media symbols, e.g. <image>, within the
conversations. On the other hand, for media without explicit
specification, this function realizes the legacy behavior to arrange
them at the beginning of the conversation.
:param conv: conversation
:param d_media: a dict of media for all media types, for determining how
many media tokens need to be inserted
"""
conv = copy.deepcopy(conv)
for media_symbol, l_media in d_media.items():
media_symbol_count = conv.count(media_symbol)
if media_symbol_count > 0:
assert media_symbol_count == len(
l_media
), f"{media_symbol_count} {media_symbol} exists in text, but {len(l_media)} actual media are given"
else:
conv = (media_symbol + " ") * len(l_media) + conv
return conv
def preprocess_item(self, data_item):
return data_item
def add_speaker_and_signal(self, source: List):
"""
Given source instruction and response pieces, return the text containing the complete conversation,
and the list of values that the model should learn to predict during training
:param source: [{"from": "human", "value": "..."}, {"from": "gpt", "value": "..."}, ...]
:return: `conversation`: string containing the complete conversation;
`to_predict_list`: the list of values that the model should learn to predict during training
"""
conv = self.conv_template()
for i, sentence in enumerate(source):
from_str = sentence["from"]
if i % 2 == 0:
assert from_str.lower() in ["human"]
role = conv.roles[0]
elif i % 2 == 1:
assert from_str.lower() in ["gpt", "assistant"]
role = conv.roles[1]
else:
raise ValueError(f"unknown dialog role: {from_str.lower()}")
value = sentence["value"]
conv.append_message(role, value)
processed = conv.process()
conversation, pieces = processed["conv"], processed["pieces"]
return conversation, pieces
def process_item(self, data_item: dict, training_mode=False) -> Tuple[List, List]:
data_item = self.preprocess_item(data_item)
d_media = self.collect_and_process_media(data_item)
source = data_item["conversations"]
# implicit_at_beginning means media without explict location specification are arranged right after bos token
# if false, then these medias are arranged at the beginning of the first question
if not self.implicit_at_beginning:
source = self.insert_implicit_media_symbol_in_q1(source, d_media)
conversation, pieces = self.add_speaker_and_signal(source)
if self.implicit_at_beginning:
conversation = self.insert_implicit_media_symbol_at_beginning(conversation, d_media)
# dialog does not need eos
tokens = self.tokenizer.encode(conversation, bos=True, eos=False)
labels = [-100 for _ in tokens]
# check special token num as expected
for media_symbol, l_media in d_media.items():
media_token = self.d_media_symbol2token[media_symbol]
media_token_count = tokens.count(media_token)
assert media_token_count == len(l_media), (
f"{media_token_count} {media_token} (for {media_symbol}) exists in tokenized conversation, "
f"but {len(l_media)} actual media are given"
)
check_pos = 0
for i, p in enumerate(pieces):
if i == 0:
tokenized_value = self.tokenizer.encode(p["data"], bos=True, eos=False)
else:
tokenized_value = self.tokenizer.encode_wo_prefix_space(p["data"])
assert (
tokens[check_pos : check_pos + len(tokenized_value)] == tokenized_value
), "inconsistent complete conversation and corresponding piece after tokenization"
if p["predict"]:
labels[check_pos : check_pos + len(tokenized_value)] = tokenized_value
check_pos = check_pos + len(tokenized_value)
if training_mode and all([_ <= 0 for _ in labels]): # nothing to predict
raise LabelAllZeroError()
# labels will be processed later by the model
tokens, labels = self.replace_media_token_with_media(tokens, labels, d_media)
assert len(tokens) == len(labels)
if training_mode:
return tokens, labels
else:
return tokens
def predict_item_token_length(self, data_item: dict) -> int:
"""
estimate the length of each item
"""
if "conversations" in data_item:
return sum([len(_["value"]) for _ in data_item["conversations"]])
else:
return 1
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