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from abc import ABC, abstractmethod
from typing import List, Dict, Union, Optional
from transformers import PretrainedConfig, AutoConfig
IGNORE_ID = -100
IMAGE_TOKEN_ID = -200
IMAGE_TOKEN = "<image>"
IMAGE_ATOM_ID = -300
IMAGE_INDICATOR_IDS = [-301, -302, -303, -304, -305]
# ----------------------------------------------------------------------
# Visual Tokenizer Configuration
# ----------------------------------------------------------------------
class BaseVisualTokenizerConfig(PretrainedConfig):
def __init__(
self,
vocab_size=16384,
tokenize_function="softmax",
tau=1.0,
depths=None,
drop_cls_token=False,
backbone_config: Optional[Union[PretrainedConfig, dict]] = None,
hidden_stride: int = 1,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.tokenize_function = tokenize_function
self.tau = tau
if isinstance(depths, str):
depths = [int(x) for x in depths.split('|')]
self.depths = depths
self.backbone_kwargs = {}
self.drop_cls_token = drop_cls_token
if backbone_config is not None:
assert isinstance(backbone_config, (PretrainedConfig, dict)), \
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
if not isinstance(backbone_config, PretrainedConfig):
model_type = backbone_config['model_type']
backbone_config.pop('model_type')
backbone_config = AutoConfig.for_model(model_type, **backbone_config)
self.backbone_config = backbone_config
self.hidden_stride = hidden_stride
class SiglipVisualTokenizerConfig(BaseVisualTokenizerConfig):
model_type = "siglip_visual_tokenizer"
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.drop_cls_token:
self.drop_cls_token = False
if self.depths:
assert len(self.depths) == 1
self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
AutoConfig.register("siglip_visual_tokenizer", SiglipVisualTokenizerConfig)
# ----------------------------------------------------------------------
# Ovis Configuration
# ----------------------------------------------------------------------
class OvisConfig(PretrainedConfig):
model_type = "ovis"
def __init__(
self,
llm_config: Optional[Union[PretrainedConfig, dict]] = None,
visual_tokenizer_config: Optional[Union[PretrainedConfig, dict]] = None,
multimodal_max_length=8192,
hidden_size=None,
conversation_formatter_class=None,
llm_attn_implementation=None,
disable_tie_weight=False,
**kwargs
):
super().__init__(**kwargs)
if llm_config is not None:
assert isinstance(llm_config, (PretrainedConfig, dict)), \
f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
if not isinstance(llm_config, PretrainedConfig):
model_type = llm_config['model_type']
llm_config.pop('model_type')
llm_config = AutoConfig.for_model(model_type, **llm_config)
self.llm_config = llm_config
if visual_tokenizer_config is not None:
assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
if not isinstance(visual_tokenizer_config, PretrainedConfig):
model_type = visual_tokenizer_config['model_type']
visual_tokenizer_config.pop('model_type')
visual_tokenizer_config = AutoConfig.for_model(model_type, **visual_tokenizer_config)
self.visual_tokenizer_config = visual_tokenizer_config
self.multimodal_max_length = multimodal_max_length
self.hidden_size = hidden_size
self.conversation_formatter_class = conversation_formatter_class
self.llm_attn_implementation = llm_attn_implementation
self.disable_tie_weight = disable_tie_weight
# ----------------------------------------------------------------------
# Conversation Formatter
# ----------------------------------------------------------------------
class ConversationFormatter(ABC):
support_tokenizer_types = None
def __init__(self, tokenizer):
tokenizer_type = type(tokenizer).__name__
assert tokenizer_type in self.support_tokenizer_types, \
f'Invalid tokenizer type, expected one from `{self.support_tokenizer_types}`, but got `{tokenizer_type}`'
self.tokenizer = tokenizer
self.image_token = IMAGE_TOKEN
self.image_token_id = IMAGE_TOKEN_ID
self.ignore_id = IGNORE_ID
def _tokenize_with_image_symbol(self, text):
text_chunks = [self.tokenizer(chunk, add_special_tokens=False).input_ids for chunk in
text.split(self.image_token)]
token_ids = []
num_chuck = len(text_chunks)
for i, chunk in enumerate(text_chunks):
token_ids.extend(chunk)
if i < num_chuck - 1:
token_ids.append(self.image_token_id)
return token_ids
@abstractmethod
def format(self, conversations: List[Dict], generation_preface=None):
pass
@abstractmethod
def format_query(self, query, generation_preface=""):
pass
class GemmaConversationFormatter(ConversationFormatter):
support_tokenizer_types = ['GemmaTokenizer', 'GemmaTokenizerFast']
def __init__(self, tokenizer):
super().__init__(tokenizer)
# Gemma does not support system prompt
self.from2role = {
"human": "<start_of_turn>user\n",
"gpt": "<start_of_turn>model\n",
}
self.gpt_token_num = None
self.im_end = "<end_of_turn>\n"
self.bos_token = "<bos>"
self.bos_token_ids = None
def format(self, conversations: List[Dict], generation_preface=None):
if self.gpt_token_num is None:
self.gpt_token_num = len(self.tokenizer(self.from2role["gpt"], add_special_tokens=False).input_ids)
if self.bos_token_ids is None:
self.bos_token_ids = self.tokenizer(self.bos_token, add_special_tokens=False).input_ids
if conversations[0]["from"] == "system":
raise ValueError("Gemma does not support system prompt")
if generation_preface is not None:
conversations.append({
"from": "gpt",
"value": generation_preface
})
prompt = "" + self.bos_token
input_ids = [] + self.bos_token_ids
labels = [] + [IGNORE_ID] * len(input_ids)
num_conversation = len(conversations)
for i, conversation in enumerate(conversations):
frm = conversation["from"]
role = self.from2role[frm]
message = conversation["value"].strip()
text = role + message
if i < num_conversation - 1 or generation_preface is None:
text += self.im_end
prompt += text
token_ids = self._tokenize_with_image_symbol(text)
input_ids.extend(token_ids)
label_ids = [self.ignore_id] * len(token_ids)
if frm == "gpt":
# learning `\n` following `im_end` is meaningless, so the last `\n` token is ignored in label
label_ids[self.gpt_token_num:-1] = token_ids[self.gpt_token_num:-1]
labels.extend(label_ids)
assert self._tokenize_with_image_symbol(prompt) == input_ids
assert len(input_ids) == len(labels)
return prompt, input_ids, labels
def format_query(self, query, generation_preface=""):
prompt, input_ids, _ = self.format([{
"from": "human",
"value": query
}], generation_preface=generation_preface)
return prompt, input_ids
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