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from abc import ABC, abstractmethod
from typing import List, Dict
from ovis.util.constants import IMAGE_TOKEN_ID, IGNORE_ID, IMAGE_TOKEN
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 QwenConversationFormatter(ConversationFormatter):
support_tokenizer_types = ['QWenTokenizer', 'Qwen2TokenizerFast']
def __init__(self, tokenizer):
super().__init__(tokenizer)
self.from2role = {
"system": "<|im_start|>system\n",
"human": "<|im_start|>user\n",
"gpt": "<|im_start|>assistant\n",
}
self.gpt_token_num = None
self.im_end = "<|im_end|>\n"
self.default_system_prompt = "You are a helpful assistant."
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 conversations[0]["from"] != "system":
conversations.insert(0, {
"from": "system",
"value": self.default_system_prompt
})
if generation_preface is not None:
conversations.append({
"from": "gpt",
"value": generation_preface
})
prompt = ""
input_ids = []
labels = []
num_conversation = len(conversations)
for i, conversation in enumerate(conversations):
frm = conversation["from"]
role = self.from2role[frm]
message = conversation["value"]
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" and generation_preface is None:
# 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
class Llama3ConversationFormatter(ConversationFormatter):
support_tokenizer_types = ['PreTrainedTokenizerFast']
def __init__(self, tokenizer):
super().__init__(tokenizer)
self.from2role = {
"system": "<|start_header_id|>system<|end_header_id|>\n\n",
"human": "<|start_header_id|>user<|end_header_id|>\n\n",
"gpt": "<|start_header_id|>assistant<|end_header_id|>\n\n",
}
self.gpt_token_num = None
self.im_end = "<|eot_id|>"
self.default_system_prompt = "You are a helpful and honest multimodal assistant."
self.bos_token = "<|begin_of_text|>"
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":
conversations.insert(0, {
"from": "system",
"value": self.default_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":
label_ids[self.gpt_token_num:] = token_ids[self.gpt_token_num:]
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
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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